Method for generating alarms/alerts based on a patient&#39;s posture and vital signs

ABSTRACT

The invention provides a body-worn monitor that measures a patient&#39;s vital signs (e.g. blood pressure, SpO2, heart rate, respiratory rate, and temperature) while simultaneously characterizing their activity state (e.g. resting, walking, convulsing, falling). The body-worn monitor processes this information to minimize corruption of the vital signs by motion-related artifacts. A software framework generates alarms/alerts based on threshold values that are either preset or determined in real time. The framework additionally includes a series of ‘heuristic’ rules that take the patient&#39;s activity state and motion into account, and process the vital signs accordingly. These rules, for example, indicate that a walking patient is likely breathing and has a regular heart rate, even if their motion-corrupted vital signs suggest otherwise.

CROSS REFERENCES TO RELATED APPLICATION

Not Applicable

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable

BACKGROUND OF THE INVENTION

Field of the Invention

The present invention relates to medical devices for monitoring vitalsigns, e.g., arterial blood pressure.

Description of the Related Art

False alarms generated by conventional vital sign monitors can representup to 90% of all alarms in critical and peri-operative care, and aretherefore a source of concern. A variety of factors cause false alarms,one of which is motion-related artifacts. Ultimately false alarms canhave a severe impact on the safety of hospitalized patients: they candesensitize medical professionals toward ‘true positive’ alarms, leadthem to set dangerously wide alarm thresholds, or even drive them tocompletely disable alarms. This can have a particularly profound impactin lower-acuity areas of the hospital, i.e. areas outside the intensivecare unit (ICU), emergency department (ED), or operating room (OR),where the ratio of medical professionals to patients can be relativelylow. In these areas a single medical professional (e.g. a nurse) oftenhas to care for a large number of patients, and necessarily relies onautomated alarms operating on vital sign monitors to effectively monitortheir patients.

Studies in critical care environments indicate that the majority offalse positive alarms are simple ‘threshold alarms’, meaning they aregenerated when a patient's vital sign exceeds a predetermined threshold.Patient motion can result in a vital sign having an erroneous high orlow value, which in turn can trigger the false alarm. In most cases,these alarms lack any real clinical meaning, and go away after about 20seconds when they are not acknowledged. Alarms can also be artificiallyinduced when a patient is moved or manipulated, or if there is an actualproblem with the vital sign monitor. False alarms due to motion-relatedartifacts are particularly very high when measured from ambulatorypatients.

Blood pressure is a vital sign that is particularly susceptible to falsealarms. In critical care environments like the ICU and OR, bloodpressure can be continuously monitored with an arterial catheterinserted in the patient's radial or femoral artery. Alternatively, bloodpressure can be measured intermittently using a pressured cuff and atechnique called oscillometry. A vital sign monitor performs both thecatheter and cuff-based measurements of blood pressure. Alternatively,blood pressure can be monitored continuously with a technique calledpulse transit time (PTT), defined as the transit time for a pressurepulse launched by a heartbeat in a patient's arterial system. PTT hasbeen shown in a number of studies to correlate to systolic (SYS),diastolic (DIA), and mean (MAP) blood pressures. In these studies, PTTis typically measured with a conventional vital signs monitor thatincludes separate modules to determine both an electrocardiogram (ECG)and pulse oximetry (SpO2). During a PTT measurement, multiple electrodestypically attach to a patient's chest to determine a time-dependent ECGcomponent characterized by a sharp spike called the ‘QRS complex’. TheQRS complex indicates an initial depolarization of ventricles within theheart and, informally, marks the beginning of the heartbeat and apressure pulse that follows. SpO2 is typically measured with a bandageor clothespin-shaped sensor that attaches to a patient's finger, andincludes optical systems operating in both the red and infrared spectralregions. A photodetector measures radiation emitted from the opticalsystems that transmits through the patient's finger. Other body sites,e.g., the ear, forehead, and nose, can also be used in place of thefinger. During a measurement, a microprocessor analyses both red andinfrared radiation detected by the photodetector to determine thepatient's blood oxygen saturation level and a time-dependent waveformcalled a photoplethysmograph (‘PPG’). Time-dependent features of the PPGindicate both pulse rate and a volumetric absorbance change in anunderlying artery caused by the propagating pressure pulse.

Typical PTT measurements determine the time separating a maximum pointon the QRS complex (indicating the peak of ventricular depolarization)and a foot of the optical waveform (indicating the beginning thepressure pulse). PTT depends primarily on arterial compliance, thepropagation distance of the pressure pulse (which is closelyapproximated by the patient's arm length), and blood pressure. Toaccount for patient-dependent properties, such as arterial compliance,PTT-based measurements of blood pressure are typically ‘calibrated’using a conventional blood pressure cuff and oscillometry. Typicallyduring the calibration process the blood pressure cuff is applied to thepatient, used to make one or more blood pressure measurements, and thenleft on the patient. Going forward, the calibration blood pressuremeasurements are used, along with a change in PTT, to continuouslymeasure the patient's blood pressure (defined herein as ‘cNIBP). PTTtypically relates inversely to blood pressure, i.e., a decrease in PTTindicates an increase in blood pressure.

A number of issued U.S. Patents describe the relationship between PTTand blood pressure. For example, U.S. Pat. Nos. 5,316,008; 5,857,975;5,865,755; and 5,649,543 each describe an apparatus that includesconventional sensors that measure an ECG and PPG, which are thenprocessed to determine PTT. U.S. Pat. No. 5,964,701 describes afinger-ring sensor that includes an optical system for detecting a PPG,and an accelerometer for detecting motion.

SUMMARY OF THE INVENTION

To improve the safety of hospitalized patients, particularly those inlower-acuity areas, it is desirable to have a vital sign monitoroperating algorithms featuring: 1) a low percentage of false positivealarms/alerts; and 2) a high percentage of true positive alarms/alerts.The term ‘alarm/alert’, as used herein, refers to an audio and/or visualalarm generated directly by a monitor worn on the patient's body, oralternatively a remote monitor (e.g., a central nursing station). Toaccomplish this, the invention provides a body-worn monitor thatmeasures a patient's vital signs (e.g. SYS, DIA, SpO2, heart rate,respiratory rate, and temperature) while simultaneously characterizingtheir activity state (e.g. resting, walking, convulsing, falling). Thebody-worn monitor processes this information to minimize corruption ofthe vital signs by motion-related artifacts. A software frameworkgenerates alarms/alerts based on threshold values that are either presetor determined in real time. The framework additionally includes a seriesof ‘heuristic’ rules that take the patient's activity state and motioninto account, and process the vital signs accordingly. These rules, forexample, indicate that a walking patient is likely breathing and has aregular heart rate, even if their motion-corrupted vital signs suggestotherwise.

The body-worn monitor features a series of sensors that measuretime-dependent PPG, ECG, motion (ACC), and pressure waveforms tocontinuously monitor a patient's vital signs, degree of motion, postureand activity level. Blood pressure, a vital sign that is particularlyuseful for characterizing a patient's condition, is typically calculatedfrom a PTT value determined from the PPG and ECG waveforms. Oncedetermined, blood pressure and other vital signs can be furtherprocessed, typically with a server within a hospital, to alert a medicalprofessional if the patient begins to decompensate.

In other embodiments, PTT can be calculated from time-dependentwaveforms other than the ECG and PPG, and then processed to determineblood pressure. In general, PTT can be calculated by measuring atemporal separation between features in two or more time-dependentwaveforms measured from the human body. For example, PTT can becalculated from two separate PPGs measured by different optical sensorsdisposed on the patient's fingers, wrist, arm, chest, or virtually anyother location where an optical signal can be measured using atransmission or reflection-mode optical configuration. In otherembodiments, PTT can be calculated using at least one time-dependentwaveform measured with an acoustic sensor, typically disposed on thepatient's chest. Or it can be calculated using at least onetime-dependent waveform measured using a pressure sensor, typicallydisposed on the patient's bicep, wrist, or finger. The pressure sensorcan include, for example, a pressure transducer, piezoelectric sensor,actuator, polymer material, or inflatable cuff.

In one aspect, the invention provides a system for processing at leastone vital sign from a patient along with a motion parameter and, inresponse, generating an alarm. The system features two sensors tomeasure the vital sign, each with a detector configured to detect atime-dependent physiological waveform indicative of one or morecontractile properties of the patient's heart. The contractile property,for example, can be a beat, expansion, contraction, or anytime-dependent variation of the heart that launches both electricalsignals and a bolus of blood. The physiological waveform, for example,can be an ECG waveform measured from any vector on the patient, a PPGwaveform, an acoustic waveform measured with a microphone, or a pressurewaveform measured with a transducer. In general, these waveforms can bemeasured from any location on the patient. The system includes at leasttwo motion-detecting sensors (e.g. analog or digital accelerometers)positioned on locations selected from a forearm, upper arm, and a bodylocation other than the forearm or upper arm of the patient. Here,‘forearm’ means any portion of the arm below the elbow, e.g. theforearm, wrist, hand, and fingers. ‘Upper arm’ means any portion of thearm above and including the elbow, e.g. the bicep, shoulder, and armpit.Each of the motion-detecting sensors generate at least one motionwaveform, and typically a set of three motion waveforms (eachcorresponding to a different axis), indicative of motion of the locationon the patient's body to which it is affixed.

A processing component (e.g., an algorithm or any computation functionoperating on a microprocessor or similar logic device in the wrist-worntransceiver) receives and processes the time-dependent physiological andmotion waveforms. The processing component performs the following steps:(i) calculates at least one vital sign (e.g., SYS, DIA, SpO2, heartrate, and respiratory rate) from the first and second time-dependentphysiological waveforms; and (ii) calculates at least one motionparameter (e.g. posture, activity state, arm height, and degree ofmotion) from the motion waveforms. A second processing component, whichcan be another algorithm or computational function operating on themicroprocessor, receives the vital sign and motion parameter anddetermines: (i) a first alarm condition, calculated by comparing thevital sign to an alarm threshold; (ii) a second alarm condition,calculated from the motion parameter; and (iii) an alarm rule,determined by collectively processing the first and second alarmconditions with an alarm algorithm. The alarm rule indicates, e.g.,whether or not the system generates an alarm.

In embodiments, the motion parameter corresponds to one of the followingactivities or postures: resting, moving, sitting, standing, walking,running, falling, lying down, and convulsing. Typically the alarm ruleautomatically generates the alarm if the motion parameter is one offalling or convulsing, as these activities typically require immediatemedical attention. If the motion parameter corresponds to walking ormost ambulatory motions, then the alarm rule does not necessarilygenerate an alarm for vital signs such as heart rate, respiratory rate,and SpO2. Here, the patient is assumed to be in a relatively safe statesince they are walking. However, even while the patient is in thisactivity state, the alarm rule can still generate an alarm if the heartrate exceeds an alarm threshold that is increased relative to itsinitial value. If the motion parameter corresponds to standing, and thevital sign is blood pressure, then the alarm rule can generate the alarmif the blood pressure exceeds an alarm threshold that is decreasedrelative to its initial value. This is because it is relatively normalfor a patient's blood pressure to safely drop as the move from a sittingor lying posture to a standing posture.

In embodiments, the vital sign is blood pressure determined from a timedifference (e.g. a PTT value) between features in the ECG and PPGwaveforms, or alternatively using features between any combination oftime-dependent ECG, PPG, acoustic, or pressure waveforms. This includes,for example, two PPG waveforms measured from different locations on thepatient's body. The motion parameter can be calculated by processingeither a time or frequency-dependent component from at least one motionwaveform. For example, the processing component can determine that thepatient is walking, convulsing, or falling by: i) calculating afrequency-dependent motion waveform (e.g. a power spectrum of atime-dependent motion waveform); and ii) analyzing a band of frequencycomponents from the frequency-dependent waveform. A band of frequencycomponents between 0-3 Hz typically indicates that the patient iswalking, while a similar band between 0-10 Hz typically indicates thatthe patient is convulsing. Finally, a higher-frequency band between 0-15Hz typically indicates that a patient is falling. In this last case, thetime-dependent motion waveform typically includes a signature (e.g. arapid change in value) that can be further processed to indicatefalling. Typically this change represents at least a 50% change in themotion waveform's value within a time period of less than 2 seconds. Inother embodiments, the first processing component determines the motionparameter by comparing a parameter determined from the motion waveform(e.g., from a time or frequency-dependent parameter of the waveform) toa pre-determined receiver operating characteristic (“ROC”) thresholdvalue associated with a pre-determined ROC curve.

In embodiments, both the first and second processing components arealgorithms or computational functions operating on one or moremicroprocessors. Typically the processing components are algorithmsoperating on a common microprocessor worn on the patient's body.Alternatively, the first processing component is an algorithm operatingon a processor worn on the patient's body, and the second processingcomponent is an algorithm operating on a remote computer (located, e.g.,at a central nursing station).

In another aspect, the invention provides a method for continuouslymonitoring a patient featuring the following steps: (i) detecting firstand second time-dependent physiological waveforms indicative of one ormore contractile properties of the patient's heart with first and secondbody-worn sensors; (ii) detecting sets of time-dependent motionwaveforms with at least two body-worn, motion-detecting sensors; (iii)processing the first and second time-dependent physiological waveformsto determine at least one vital sign from the patient; (iv) analyzing aportion of the sets of time-dependent motion waveforms with amotion-determining algorithm to determine the patient's activity state(e.g. resting, moving, sitting, standing, walking, running, falling,lying down, and convulsing); and (v) generating an alarm by processingthe patient's activity state and comparing the vital sign to apredetermined alarm criteria corresponding to this state.

In embodiments, the analyzing step features calculating a mathematicaltransform (e.g. a Fourier Transform) of at least one time-dependentmotion waveform to determine a frequency-dependent motion waveform (e.g.a power spectrum), and then analyzing frequency bands in this waveformto determine if the patient is walking, convulsing, or falling. Thisstep can also include calculating a time-dependent change or variationin the time-dependent waveforms, e.g. a standard deviation, mathematicalderivative, or a related statistical parameter. In other embodiments,the analyzing step includes determining the motion parameter bycomparing a time-dependent motion waveform to a mathematical functionusing, e.g., a numerical fitting algorithm such as a linear leastsquares or Marquardt-Levenberg non-linear fitting algorithm.

The analyzing step can include calculating a ‘logit variable’ from atleast one time-dependent motion waveform, or a waveform calculatedtherefrom, and comparing the logit variable to a predetermined ROC curveto determine the patient's activity state. For example, the logitvariable can be calculated from at least one time or frequency-dependentmotion waveform, or a waveform calculated therefrom, and then comparedto different ROC curves corresponding to various activity and posturestates.

In another aspect, the invention provides a system for continuouslymonitoring a group of patients, wherein each patient in the group wearsa body-worn monitor similar to those described herein. Additionally,each body-worn monitor is augmented with a location sensor. The locationsensor includes a wireless component and a location processing componentthat receives a signal from the wireless component and processes it todetermine a physical location of the patient. A processing component(similar to that described above) determines from the time-dependentwaveforms at least one vital sign, one motion parameter, and an alarmparameter calculated from the combination of this information. Awireless transceiver transmits the vital sign, motion parameter,location of the patient, and alarm parameter through a wireless system.A remote computer system featuring a display and an interface to thewireless system receives the information and displays it on a userinterface for each patient in the group.

In embodiments, the user interface is a graphical user interfacefeaturing a field that displays a map corresponding to an area withmultiple sections. Each section corresponds to the location of thepatient and includes, e.g., the patient's vital signs, motion parameter,and alarm parameter. For example, the field can display a mapcorresponding to an area of a hospital (e.g. a hospital bay or emergencyroom), with each section corresponding to a specific bed, chair, orgeneral location in the area. Typically the display renders graphicalicons corresponding to the motion and alarm parameters for each patientin the group. In other embodiments, the body-worn monitor includes agraphical display that renders these parameters directly on the patient.

Typically the location sensor and the wireless transceiver operate on acommon wireless system, e.g. a wireless system based on 802.11,802.15.4, or cellular protocols. In this case a location is determinedby processing the wireless signal with one or more algorithms known inthe art. These include, for example, triangulating signals received fromat least three different base stations, or simply estimating a locationbased on signal strength and proximity to a particular base station. Instill other embodiments the location sensor includes a conventionalglobal positioning system (GPS).

The body-worn monitor can include a first voice interface, and theremote computer can include a second voice interface that integrateswith the first voice interface. The location sensor, wirelesstransceiver, and first and second voice interfaces can all operate on acommon wireless system, such as one of the above-described systems basedon 802.11 or cellular protocols. The remote computer, for example, canbe a monitor that is essentially identical to the monitor worn by thepatient, and can be carried or worn by a medical professional. In thiscase the monitor associate with the medical professional features adisplay wherein the user can select to display information (e.g. vitalsigns, location, and alarms) corresponding to a particular patient. Thismonitor can also include a voice interface so the medical professionalcan communicate with the patient.

In another aspect, the invention provides a body-worn monitor featuringoptical sensor that measures two time-dependent optical waveforms (e.g.PPG waveforms) from the patient's body, and an electrical sensorfeaturing at least two electrodes and an electrical circuit thatcollectively measure a first time-dependent electrical waveform (e.g.,an ECG waveform) indicating the patient's heart rate, and a secondtime-dependent electrical waveform (e.g. a waveform detected withimpedance pneumography) indicating the patient's respiratory rate. Themonitor includes at least two motion-detecting sensors positioned on twoseparate locations on the patient's body. A processing component,similar to that described above, determines: (i) a time differencebetween features in one of the time-dependent optical and electricalwaveforms; (ii) a blood pressure value calculated from the timedifference; iii) an SpO2 value calculated from both the first and secondoptical waveforms; (iii) a heart rate calculated from one of thetime-dependent electrical waveforms; (iv) a respiratory rate calculatedfrom the second time-dependent electrical waveform; (v) at least onemotion parameter calculated from at least one motion waveform; and (vi)an alarm parameter calculated from at least one of the blood pressurevalue, SpO2 value, heart rate, respiratory rate, and the motionparameter.

In embodiments, the processing component renders numerical valuescorresponding to the blood pressure value, SpO2 value, heart rate, andrespiratory rate on a graphical display. These parameters, however, arenot rendered when the motion parameter corresponds to a moving patient(e.g. a walking patient). Using the motion waveforms, the monitor candetect when the patient is lying down, and from the electrical waveformsif their respiratory rate has ceased for an extended period of time(e.g. at least 20 seconds). In this case, for example, the processingcomponent can generate an alarm parameter corresponding to apnea. Thetime-dependent electrical waveforms can be further processed todetermine heart rate along with an additional parameter, such as VFIB,VTAC, and ASY, defined in detail below. Similarly, the processingcomponent can analyze the time-dependent optical waveforms to determinea pulse rate, and can determine a pulse pressure from a differencebetween diastolic and systolic blood pressures. It determines a‘significant pulse rate’ if the pulse rate is greater than 30 beats perminute, and the pulse pressure is greater than 10 mmHg. The monitor thengenerates an alarm parameter corresponding to one of VFIB, VTAC, and ASYif these parameters are determined from the patient and a significantpulse rate is not present.

In other embodiments, the processing component can process at least onemotion waveform to determine a number of times the patient moves fromlying in a first position to lying in a different position, and generatean alarm parameter if the number is less than a threshold value (e.g.once per four hours). Such an alarm indicates, for example, a ‘bed soreindex’, i.e. an index that indicates when the patient may developlesions due to inactivity. The monitor can also include a temperaturesensor, configured, e.g., to attach to a portion of the patient's chest.

In another aspect, the invention provides a body-worn monitor describedabove for monitoring a patient's vital signs using time-dependent ECGand PPG waveforms. The processing component determines at least onemotion parameter measured by a motion-detecting sensor (e.g. anaccelerometer) representing the patient's posture, activity state, anddegree of motion. The motion parameter is calculated by comparing acomponent determined from a time or frequency-dependent waveform or aROC curve to a predetermined threshold value. An alarm is generated bycollectively processing a vital sign and the motion parameter with analarm algorithm. The monitor can include a graphical display, worn onthe patient's body, which renders numerical values indicating thepatient's vital signs, and iconic images indicating both the motionparameter and the alarm. The graphical display typically includes afirst user interface for a patient, and a second user interface for amedical professional that is rendered after the processing unitprocesses an identifier (e.g. a barcode or radio frequencyidentification, or RFID) corresponding to the medical professional. Thebody-worn monitor can also include a wireless transceiver that transmitsthe vital sign, motion parameter, and alarm to a remote computer whichfurther includes a graphical display for rendering this information.

In another aspect, the invention provides a method for generating analarm while monitoring vital signs and posture of a patient. A monitor,similar to that described above, measures vital signs fromtime-dependent waveforms (e.g. any combination of optical, electrical,acoustic, or pressure waveforms) and a patient's posture with at leastone motion-detecting sensor positioned on the patient's torso (e.g., anaccelerometer positioned on the patient's chest). The processingcomponent analyzes at least a portion of a set of time-dependent motionwaveforms generated by the motion-detecting sensor to determine a vectorcorresponding to motion of the patient's torso. It then compares thevector to a coordinate space representative of how the motion-detectingsensor is oriented on the patient to determine a posture parameter,which it then processes along with the vital sign to generate an alarm.The alarm, for example, is indicated by a variance of the vital signrelative to a predetermined alarm criterion, and is regulated accordingto the patient's posture.

In embodiments, the method generates the alarm in response to a changein the patient's posture, e.g. if the patient is standing up, or iftheir posture changes from lying down to either sitting or standing up,or from standing up to either sitting or lying down.

To determine the vector the method includes an algorithm or computationfunction that analyzes three time-dependent motion waveforms, eachcorresponding to a unique axis of the motion-detecting sensor. Themotion waveforms can yield three positional vectors that define acoordinate space. In a preferred embodiment, for example, the firstpositional vector corresponds to a vertical axis, a second positionalvector corresponds to a horizontal axis, and the third positional vectorcorresponds to a normal axis extending normal from the patient's chest.Typically the posture parameter is an angle, e.g. an angle between thevector and at least one of the three positional vectors. For example,the angle can be between the vector and a vector corresponding to avertical axis. The patient's posture is estimated to be upright if theangle is less than a threshold value that is substantially equivalent to45 degrees (e.g., 45 degrees+/−10 degrees); otherwise, the patient'sposture is estimated to be lying down. If the patient is lying down, themethod can analyze the angle between the vector and a vectorcorresponding to a normal axis extending normal from the patient'schest. In this case, the patient's posture is estimated to be supine ifthe angle is less than a threshold value substantially equivalent to 35degrees (e.g., 35 degrees+/−10 degrees), and prone if the angle isgreater than a threshold value substantially equivalent to 135 degrees(e.g., 135 degrees+/−10 degrees). Finally, if the patient is lying down,the method can analyze the angle between the vector and a vectorcorresponding to a horizontal axis. In this case, the patient isestimated to be lying on a first side if the angle is less than athreshold value substantially equivalent to 90 degrees (e.g., 90degrees+/−10 degrees), and lying on an opposing side if the angle isgreater than a threshold value substantially equivalent to 90 degrees(e.g., 90 degrees+/−10 degrees).

Blood pressure is determined continuously and non-invasively using atechnique, based on PTT, which does not require any source for externalcalibration. This technique, referred to herein as the ‘compositetechnique’, operates on the body-worn monitor and wirelessly transmitsinformation describing blood pressure and other vital signs to theremote monitor. The composite technique is described in detail in theco-pending patent application entitled: VITAL SIGN M FOR MEASURING BLOODPRESSURE USING OPTICAL, ELECTRICAL, AND PRESSURE WAVEFORMS (U.S. Ser.No. 12/138,194; filed Jun. 12, 2008), the contents of which are fullyincorporated herein by reference.

Still other embodiments are found in the following detailed descriptionof the invention, and in the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic drawing of a body-worn monitor featuring threeaccelerometers for detecting motion, along with ECG, optical, andpneumatic systems for measuring vital signs;

FIG. 2 shows a graph of time-dependent waveforms (ECG, PPG, and ACC)generated from a resting patient by, respectively, the ECG system, theoptical system, and the accelerometer system of FIG. 1;

FIG. 3 shows a graph of time-dependent waveforms (ECG, PPG, and ACC)generated from a walking patient by, respectively, the ECG system, theoptical system, and the accelerometer system of FIG. 1;

FIG. 4 shows a graph of time-dependent waveforms (ECG, PPG, and ACC)generated from a convulsing patient by, respectively, the ECG system,the optical system, and the accelerometer system of FIG. 1;

FIG. 5 shows a graph of time-dependent waveforms (ECG, PPG, and ACC)generated from a falling patient by, respectively, the ECG system, theoptical system, and the accelerometer system of FIG. 1;

FIG. 6 shows graphs of frequency-dependent power spectra generated fromthe time-dependent ACC waveforms of FIGS. 2-5;

FIG. 7 shows a graph of the first 20 Hz of three of thefrequency-dependent power spectra of FIG. 6;

FIG. 8 shows a flow chart describing an algorithm used to generatealarms/alerts using the body-worn monitor of FIG. 1;

FIG. 9 shows a series of icons used to indicate different types ofpatient motion in graphical user interfaces (GUI) rendered on thebody-worn monitor and remote monitor;

FIGS. 10A and 10B show, respectively, patient and map views used in theGUI rendered on the remote monitor;

FIGS. 11A and 11B show, respectively, patient and medical professionalviews used in the GUI rendered on the body-worn monitor;

FIG. 12 shows a schematic drawing of a coordinate system used tocalibrate accelerometers used in the body-worn monitor of FIG. 1;

FIG. 13 shows a schematic drawing of three accelerometers attached to apatient's arm and connected to the body-worn monitor of FIG. 1;

FIG. 14 is a graph of time-dependent waveforms indicating a patient'selbow height and corresponding PTT;

FIG. 15 shows a schematic drawing of a coordinate system representing anaccelerometer coordinate space superimposed on a patient's torso;

FIG. 16 shows the accelerometer coordinate space of FIG. 15 and a vectorrepresenting the direction and magnitude of gravity, along with anglesseparating the vector from each axis of the coordinate system;

FIG. 17A is a graph showing time-dependent motion waveformscorresponding to different posture states and measured with anaccelerometer positioned on a patient's chest;

FIG. 17B is a graph showing posture states calculated using thetime-dependent motion waveforms of FIG. 17A and a mathematical model fordetermining a patient's posture;

FIG. 18 is a schematic drawing of a calculation used to determine a typeof activity exhibited by a patient;

FIGS. 19A and 19B are receiver operating characteristic (ROC) curvescharacterizing, respectively, a patient that is walking and resting;

FIGS. 20A and 20B show images of the body-worn monitor of FIG. 1attached to a patient with and without, respectively, a cuff-basedpneumatic system used for a calibrating indexing measurement; and

FIG. 21 shows an image of the wrist-worn transceiver featured in thebody-worn monitor of FIGS. 20A and 20B.

DETAILED DESCRIPTION OF THE INVENTION

System Overview

FIG. 1 shows a schematic drawing of a body-worn monitor 10 according tothe invention featuring a wrist-worn transceiver 12 that continuouslydetermines vital signs (e.g. SYS, DIA, SpO2, heart rate, respiratoryrate, and temperature) and motion (e.g. posture, arm height, activitylevel, and degree of motion) for, e.g., an ambulatory patient in ahospital. The monitor 10 is coupled to a software framework fordetermining alarms/alerts that processes both the motion and vital signinformation with algorithms that reduce the occurrence of false alarmsin, e.g., a hospital. The transceiver 12 connects to three separateaccelerometers 14 a, 14 b, 14 c distributed along a patient's arm andtorso and connected to a single cable. Each of these sensors measuresthree unique ACC waveforms, each corresponding to a separate axis (x, y,or z), which are digitized internally and sent to a computer processingunit (CPU) 22 within the transceiver 12 for processing. The transceiver12 also connects to an ECG system 16 that measures an ECG waveform, anoptical system 18 that measures a PPG waveform, and a pneumatic system20 for making cuff-based ‘indexing’ blood pressure measurementsaccording to the composite technique. Collectively, these systems 14a-c, 16, 18, and 20 continuously measure the patient's vital signs andmotion, and supply information to the software framework that calculatesalarms/alerts.

The ECG 16 and pneumatic 20 systems are stand-alone systems that includea separate microprocessor and analog-to-digital converter. During ameasurement, they connect to the transceiver 12 through connectors 28,30 and supply digital inputs using a communication protocol that runs ona controller-area network (CAN) bus. The CAN bus is a serial interface,typically used in the automotive industry, which allows differentelectronic systems to effectively communicate with each other, even inthe presence of electrically noisy environments. A third connector 32also supports the CAN bus and is used for ancillary medical devices(e.g. a glucometer) that is either worn by the patient or present intheir hospital room.

The optical system 18 features an LED and photodetector and, unlike theECG 16 and pneumatic 20 systems, generates an analog electrical signalthat connects through a cable 36 and connector 26 to the transceiver 12.As is described in detail below, the optical 18 and ECG 16 systemsgenerate synchronized time-dependent waveforms that are processed withthe composite technique to determine a PTT-based blood pressure alongwith motion information.

The first accelerometer 14 a is surface-mounted on a printed circuitedboard within the transceiver 12, which is typically worn on thepatient's wrist like a watch. The second 14 b accelerometer is typicallydisposed on the upper portion of the patient's arm and attaches to acable 40 that connects the ECG system 16 to the transceiver 12. Thethird accelerometer 14 c is typically worn on the patient's chestproximal to the ECG system 16. The second 14 b and third 14 caccelerometers integrate with the ECG system 16 into a single cable 40,as is described in more detail below, which extends from the patient'swrist to their chest and supplies digitized signals over the CAN bus. Intotal, the cable 40 connects to the ECG system 16, two accelerometers 14b, 14 c, and at least three ECG electrodes (shown in FIGS. 20A and 20B,and described in more detail below). The cable typically includes 5separate wires bundled together with a single protective cladding: thewires supply power and ground to the ECG system 16 and accelerometers 14b, 14 c, provide high signal and low signal transmission lines for datatransmitted over the CAN protocol, and provide a grounded electricalshield for each of these four wires. It is held in place by the ECGelectrodes, which are typically disposable and feature an adhesivebacking, and a series of bandaid-like disposable adhesive strips. Thissimplifies application of the system and reduces the number of sensingcomponents attached to the patient.

To determine posture, arm height, activity level, and degree of motion,the transceiver's CPU 22 processes signals from each accelerometer 14a-c with a series of algorithms, described in detail below. In total,the CPU can process nine unique, time-dependent signals (ACC₁₋₉)corresponding to the three axes measured by the three separateaccelerometers. Specifically, the algorithms determine parameters suchas the patient's posture (e.g., sitting, standing, walking, resting,convulsing, falling), the degree of motion, the specific orientation ofthe patient's arm and how this affects vital signs (particularly bloodpressure), and whether or not time-dependent signals measured by the ECG16, optical 18, or pneumatic 20 systems are corrupted by motion. Oncethis is complete, the transceiver 12 uses an internal wirelesstransmitter 24 to send information in a series of packets, as indicatedby arrow 34, to a remote monitor within a hospital. The wirelesstransmitter 24 typically operates on a protocol based on 802.11 andcommunicates with an existing network within the hospital. Thisinformation alerts a medical professional, such as a nurse or doctor, ifthe patient begins to decompensate. A server connected to the hospitalnetwork typically generates this alarm/alert once it receives thepatient's vital signs, motion parameters, ECG, PPG, and ACC waveforms,and information describing their posture, and compares these parametersto preprogrammed threshold values. As described in detail below, thisinformation, particularly vital signs and motion parameters, is closelycoupled together. Alarm conditions corresponding to mobile andstationary patients are typically different, as motion can corrupt theaccuracy of vital signs (e.g., by adding noise), and induce changes inthem (e.g., through acceleration of the patient's heart and respiratoryrates).

General Methodology for Alarms/Alerts

Algorithms operating on either the body-worn monitor or remote monitorgenerate alarms/alerts that are typically grouped into three generalcategories: 1) motion-related alarms/alerts indicating the patient isexperiencing a traumatic activity, e.g. falling or convulsing; 2)life-threatening alarms/alerts typically related to severe eventsassociated with a patient's cardiovascular or respiratory systems, e.g.asystole (ASY), ventricular fibrillation (VFIB), ventricular tachycardia(VTAC), and apnea (APNEA); and 3) threshold alarms/alerts generated whenone of the patient's vital signs (SYS, DIA, SpO2, heart rate,respiratory rate, or temperature) exceeds a threshold that is eitherpredetermined or calculated directly from the patient's vital signs. Thegeneral methodology for generating alarms/alerts in each of thesecategories is described in more detail below.

Motion-Related Alarms/Alerts

FIGS. 2-5 show time-dependent graphs of ECG, PPG, and ACC waveforms fora patient who is resting (FIG. 2), walking (FIG. 3), convulsing (FIG.4), and falling (FIG. 5). Each graph includes a single ECG waveform 50,55, 60, 65, PPG waveform 51, 56, 61, 66, and three ACC waveforms 52, 57,62, 67. The ACC waveforms correspond to signals measured along the x, y,and z axes by a single accelerometer worn on the patient's wrist (e.g.,ACC₁₋₃). The body-worn monitor includes additional accelerometers(typically worn on the patient's bicep and chest) that measure theremaining six ACC waveforms (e.g., ACC₄₋₉). Sensors that measure theECG, PPG, and ACC waveforms are shown in FIGS. 20A, 20B, and 21, anddescribed in detail below.

The figures indicate that time-dependent properties of both ECG 50, 55,60, 65 and PPG 51, 56, 61, 66 waveforms are strongly affected by motion,as indicated by the ACC waveforms 52, 57, 62, 67. Accuracy of the vitalsigns, such as SYS, DIA, heart rate, respiratory rate, and SpO2,calculated from these waveforms is therefore affected as well. Bodytemperature, which is measured from a separate body-worn sensor(typically a thermocouple) and does not rely on these waveforms, isrelatively unaffected by motion.

FIG. 2 shows data collected from a patient at rest. This state isclearly indicated by the ACC waveforms 52, which feature a relativelystable baseline. High-frequency noise in all the ACC waveforms 52, 57,62, 67 shown in FIGS. 2-5 is due to electrical noise, and is notindicative of patient motion in any way. The ECG 50 and PPG 51 waveformsfor this patient are correspondingly stable, thus allowing algorithmsoperating on the body-worn monitor to accurately determine heart rateand respiratory rate (from the ECG waveform 50), blood pressure (from aPTT extracted from both the ECG 50 and PPG 51 waveforms), and SpO2 (fromPPG waveforms, similar to PPG waveform 51, measured at both 900 nm and600 nm using the finger-worn optical sensor). Respiratory rate slightlymodulates the envelope of the ECG 50 and PPG 51 waveforms. Based on thedata shown in FIG. 2, algorithms operating on the body-worn monitorassume that vital signs calculated from a resting patient are relativelystable; the algorithm therefore deploys normal threshold criteria foralarms/alerts, described below in Table 2, for patients in this state.

FIG. 3 shows ECG 55, PPG 56, and ACC 57 waveforms measured from awalking patient wearing the body-worn monitor. In this case, the ACCwaveform 57 clearly indicates a quasi-periodic modulation, with each‘bump’ in the modulation corresponding to a particular step. The ‘gaps’in the modulation, shown near 10, 19, 27, and 35 seconds, correspond toperiods when the patient stops walking and changes direction. Each bumpin the ACC waveform includes relatively high-frequency features (otherthan those associated with electrical noise, described above) thatcorrespond to walking-related movements of the patient's wrist.

The ECG waveform 55 measured from the walking patient is relativelyunaffected by motion, other than indicating an increase in heart rate(i.e., a shorter time separation between neighboring QRS complexes) andrespiratory rate (i.e. a higher frequency modulation of the waveform'senvelope) caused by the patient's exertion. The PPG waveform 56, incontrast, is strongly affected by this motion, and becomes basicallyimmeasurable. Its distortion is likely due to a quasi-periodic change inlight levels, caused by the patient's swinging arm, and detected by theoptical sensor's photodetector. Movement of the patient's armadditionally affects blood flow in the thumb and can cause the opticalsensor to move relative to the patient's skin. The photodetectormeasures all of these artifacts, along with a conventional PPG signal(like the one shown in FIG. 2) caused by volumetric expansion in theunderlying arteries and capillaries within the patient's thumb. Theartifacts produce radiation-induced photocurrent that is difficult todeconvolute from normal PPG signal used to calculate PTT-based bloodpressure and SpO2. These vital signs, and particularly blood pressurebecause of its sensitivity to temporal separation from the ECG's QRScomplex, are thus difficult to measure when the patient is walking.

The body-worn monitor deploys multiple strategies to avoid generatingfalse alarms/alerts during a walking activity state. As described indetail below, the monitor can detect this state by processing the ACCwaveforms shown in FIG. 3 along with similar waveforms measured from thepatient's bicep and chest. As described in Table 1A, walking typicallyelevates heart rate, respiratory rate, and blood pressure, and thusalarm thresholds for these parameters are systematically and temporarilyincreased when this state is detected. Values above the modifiedthresholds are considered abnormal, and trigger an alarm. PTT-based SYSand DIA are difficult to measure from a walking patient, andalternatively can be measured directly from the ECG waveform. Anaccurate measurement of SpO2 depends on relative optical absorptionmeasurements made at both 900 and 600 nm, and does not necessarily relyon having a PPG waveform that is completely free of motion-relatedartifacts. Still, it is more difficult to measure an accurate value ofSpO2 when a patient is walking. Moreover, SpO2, unlike heart rate,respiratory rate and blood pressure, does not typically increase withexertion. Thus the alarm thresholds for this parameter, as shown inTable 1A, do not change when the patient is walking. Body temperaturemeasured with the body-worn monitor typically increases between 1-5%,depending on the physical condition of the patient and the speed atwhich they are walking.

TABLE 1A motion-dependent alarm/alert thresholds and heuristic rules fora walking patient Modified Heuristic Motion Threshold for Rules forVital Sign State Alarms/Alerts Alarms/Alerts Blood Pressure WalkingIncrease (+10-30%) Use Modified (SYS, DIA) Threshold; Alarm/Alert ifValue Exceeds Threshold Heart Rate Walking Increase (+10-300%) IgnoreThreshold; Do Not Alarm/Alert Respiratory Rate Walking Increase(+10-300%) Ignore Threshold; Do Not Alarm/Alert SpO2 Walking No ChangeIgnore Threshold; Do Not Alarm/Alert Temperature Walking Increase(+10-30%) Use Original Threshold; Alarm/Alert if Value Exceeds Threshold

To further reduce false alarms/alerts, software associated with thebody-worn monitor or remote monitor can deploy a series of heuristicrules determined beforehand using practical, empirical studies. Theserules, for example, can indicate that a walking patient is likelyhealthy, breathing, and characterized by a normal SpO2. Accordingly, therules dictate that respiratory rate and SpO2 values that are measuredduring a walking state and exceed predetermined alarm/alert thresholdsare likely corrupted by artifacts; the system, in turn, does not soundthe alarm/alert in this case. Heart rate, as indicated by FIG. 2, andbody temperature can typically be accurately measured even when apatient is walking; the heuristic rules therefore dictate thatalarms/alerts can be generated from these vital signs, but that themodified thresholds listed in Table 1A be used for this process.

FIG. 4 shows ECG 60, PPG 61, and ACC 62 waveforms measured from apatient that is simulating convulsing by rapidly moving their arm backand forth. A patient undergoing a Grand-mal seizure, for example, wouldexhibit this type of motion. As is clear from the waveforms, the patientis at rest for the initial 10 seconds shown in the graph, during whichthe ECG 60 and PPG 61 waveforms are uncorrupted by motion. The patientthen begins a period of simulated, rapid convulsing that lasts for about12 seconds. A brief 5-second period of rest follows, and then convulsingbegins for another 12 seconds or so.

Convulsing modulates the ACC waveform 62 due to rapid motion of thepatient's arm, as measured by the wrist-worn accelerometer. Thismodulation is strongly coupled into the PPG waveform 61, likely becauseof the phenomena described above, i.e.: 1) ambient light coupling intothe optical sensor's photodiode; 2) movement of the photodiode relativeto the patient's skin; and 3) disrupted blow flow underneath the opticalsensor. Note that from about 23-28 seconds the ACC waveform 62 is notmodulated, indicating that the patient's arm is at rest. During thisperiod the ambient light is constant and the optical sensor isstationary relative to the patient's skin. But the PPG waveform 61 isstill strongly modulated, albeit at a different frequency than themodulation that occurred when the patient's arm was moving. Thisindicates modulation of the PPG waveform 61 is likely caused by at leastthe three factors described above, and that disrupted blood flowunderneath the optical sensor continues even after the patient's armstops moving. Using this information, both ECG and PPG waveforms similarto those shown in FIG. 4 can be analyzed in conjunction with ACCwaveforms measured from groups of stationary and moving patients. Thesedata can then be analyzed to estimate the effects of specific motionsand activities on the ECG and PPG waveforms, and then deconvolute themusing known mathematical techniques to effectively remove anymotion-related artifacts. The deconvoluted ECG and PPG waveforms canthen be used to calculate vital signs, as described in detail below.

The ECG waveform 60 is modulated by the patient's arm movement, but to alesser degree than the PPG waveform 61. In this case, modulation iscaused primarily by electrical ‘muscle noise’ instigated by theconvulsion and detected by the ECG electrodes, and well as byconvulsion-induced motion in the ECG cables and electrodes relative tothe patient's skin. Such motion is expected to have a similar affect ontemperature measurements, which are determined by a sensor that alsoincludes a cable.

Table 1B, below, shows the modified threshold values and heuristic rulesfor alarms/alerts generated by a convulsing patient. In general, when apatient experiences convulsions, such as those simulated during the two12-second periods in FIG. 4, it is virtually impossible to accuratelymeasure any vital signs from the ECG 60 and PPG 61 waveforms. For thisreason the threshold values corresponding to each vital sign are notadjusted when convulsions are detected. Heart rate determined from theECG waveform, for example, is typically erroneously high due tohigh-frequency convulsions, and respiratory rate is immeasurable fromthe distorted waveform. Strong distortion of the optical waveform alsomakes both PPT-based blood pressure and SpO2 difficult to measure. Forthis reason, algorithms operating on either the body-worn monitor or aremote monitor will not generate alarms/alerts based on vital signs whena patient is convulsing, as these vital signs will almost certainly becorrupted by motion-related artifacts.

TABLE 1B motion-dependent alarm/alert thresholds and heuristic rules fora convulsing patient Modified Heuristic Motion Threshold for Rules forVital Sign State Alarms/Alerts Alarms/Alerts Blood Pressure ConvulsingNo Change Ignore Threshold; (SYS, DIA) Generate Alarm/Alert Because ofConvulsion Heart Rate Convulsing No Change Ignore Threshold; GenerateAlarm/Alert Because of Convulsion Respiratory Rate Convulsing No ChangeIgnore Threshold; Generate Alarm/Alert Because of Convulsion SpO2Convulsing No Change Ignore Threshold; Generate Alarm/Alert Because ofConvulsion Temperature Convulsing No Change Ignore Threshold; GenerateAlarm/Alert Because of Convulsion

Table 1B also shows the heuristic rules for convulsing patients. Here,the overriding rule is that a convulsing patient needs assistance, andthus an alarm/alert for this patient is generated regardless of theirvital signs (which, as described above, are likely inaccurate due tomotion-related artifacts). The system always generates an alarm/alertfor a convulsing patient.

FIG. 5 shows ECG 65, PPG 66, and ACC 67 waveforms measured from apatient that experiences a fall roughly 13 seconds into the measuringperiod. The ACC waveform 67 clearly indicates the fall with a sharpdecrease in its signal, followed by a short-term oscillatory signal, due(literally) to the patient bouncing on the floor. After the fall, ACCwaveforms 67 associated with the x, y, and z axes also show a prolongeddecrease in value due to the resulting change in the patient's posture.In this case, both the ECG 65 and PPG 66 waveforms are uncorrupted bymotion prior to the fall, but basically immeasurable during the fall,which typically takes only 1-2 seconds. Specifically, this activity addsvery high frequency noise to the ECG waveform 65, making it impossibleto extract heart rate and respiratory rate during this short timeperiod. Falling causes a sharp drop in the PPG waveform 66, presumablyfor the same reasons as described above (i.e. changes in ambient light,sensor movement, disruption of blood flow) for walking and convulsing.

After a fall, both the ECG 65 and PPG 66 waveforms are free fromartifacts, but both indicate an accelerated heart rate and relativelyhigh heart rate variability for roughly 10 seconds. During this periodthe PPG waveform 66 also shows a decrease in pulse amplitude. Withoutbeing bound to any theory, the increase in heart rate may be due to thepatient's baroreflex, which is the body's hemostatic mechanism forregulating and maintaining blood pressure. The baroreflex, for example,is initiated when a patient begins faint. In this case, the patient'sfall may cause a rapid drop in blood pressure, thereby depressing thebaroreflex. The body responds by accelerating heart rate (indicated bythe ECG waveform 65) and increasing blood pressure (indicated by areduction in PTT, as measured from the ECG 65 and PPG 66 waveforms) inorder to deliver more blood to the patient's extremities.

Table 1C shows the heuristic rules and modified alarm thresholds for afalling patient. Falling, similar to convulsing, makes it difficult tomeasure waveforms and the vital signs calculated from them. Because ofthis and the short time duration associated with a fall, alarms/alertsbased on vital signs thresholds are not generated when a patient falls.However, this activity, optionally coupled with prolonged stationaryperiod or convulsion (both determined from the following ACC waveform),generates an alarm/alert according to the heuristic rules.

TABLE 1C motion-dependent alarm/alert thresholds and heuristic rules fora falling patient Modified Heuristic Motion Threshold for Rules forVital Sign State Alarms/Alerts Alarms/Alerts Blood Pressure Falling NoChange Ignore Threshold; Generate (SYS, DIA) Alarm/Alert Because of FallHeart Rate Falling No Change Ignore Threshold; Generate Alarm/AlertBecause of Fall Respiratory Falling No Change Ignore Threshold; GenerateRate Alarm/Alert Because of Fall SpO2 Falling No Change IgnoreThreshold; Generate Alarm/Alert Because of Fall Temperature Falling NoChange Ignore Threshold; Generate Alarm/Alert Because of Fall

As described in detail below, the patient's specific activity relates toboth the time-dependent ACC waveforms and the frequency-dependentFourier Transforms of these waveforms. FIG. 6, for example, shows powerspectra 70, 71, 72, 73 corresponding to ACC waveforms generated during,respectively, convulsing, falling, walking, and resting. These powerspectra were generated from both real and imaginary components ofFourier Transforms calculated from the corresponding time-dependentwaveforms.

The ACC waveform corresponding to a resting patient (52 in FIG. 2) lacksany time-dependent features corresponding to patient motion; thehigh-frequency features in this waveform (i.e., those greater than about20 Hz) are due solely to electrical noise generated by theaccelerometer. The power spectrum 73 shown in the lower right-handcorner of FIG. 6 thus lacks any features in a frequency range (typicallyless than 20 Hz) corresponding to human motion. In contrast, convulsingtypically represents a well-defined, quasi-periodic motion; thiscorresponds to a strong, narrow peak occurring near 6 Hz that dominatesthe power spectrum 70 shown in the upper left-hand corner of the figure.The bandwidth of this peak, which is best represented by a Gaussianfunction, indicates a distribution of frequencies centered around 6 Hz.Falling and walking, as indicated by spectra 71, 72 shown, respectively,in the upper right-hand and lower left-hand portions of the figure, aremore complicated motions. The spectrum for walking, for example, ischaracterized by relatively weak peaks occurring near 1 and 2 Hz; thesecorrespond to frequencies associated with the patient's stride. Arelatively large peak in the spectrum near 7 Hz corresponds to higherfrequency motion of the patient's hand and arm that naturally occursduring walking. Falling, unlike walking or convulsing, lacks anywell-defined periodic motion. Instead it is characterized by a sharptime-dependent change in the ACC waveform (67 in FIG. 5). This event istypically composed of a collection of relatively high-frequencycomponents, as indicated by the multiple harmonic peaks, ranging every 2Hz, between 2-12 Hz. Note that the spectral power associated withconvulsions 70 is significantly higher than that associated with bothfalling 71 and walking 72. For this reason the higher frequency spectralcomponents associated with the accelerometer's electrical noise, shownclearly in the resting power spectrum 73, are evident in these spectra71, 72, but not in the spectrum 70 for convulsions.

FIG. 7 shows a graph 80 of frequency-dependent power spectra between0-20 Hz of the falling, walking, and convulsing activities indicated inFIG. 6. This frequency range, as described above, corresponds to humanmotion. The graph 80 additionally includes a series of bars 81 dividedinto roughly 0.5 Hz increments that extend up to 10 Hz. The power of theindividual spectra in these increments, as indicated by Table 5 and usedin equations (36) and (37) below, can be processed along withtime-dependent features of the ACC waveforms to estimate the patient'sspecific activity level. The distribution of frequencies in the graph 80indicates, to some extent, how this algorithm works. For example,convulsing is typically characterized by a single well-definedfrequency, in this case centered near 6 Hz. This activity has abandwidth of approximately 1.5 Hz, and therefore yields a relativelyhigh power for the spectral increments in this range. Falling, incontrast, yields relatively equivalent power in increments ranging from2 to 10 Hz. The power spectrum corresponding to walking is relativelycomplex, resulting measurable power in low-frequency increments(typically 1-2 Hz, due to the patient's stride), and higher power inrelatively high-frequency increments (near 7 Hz, due to the patient'shand and arm motion). To characterize a patient's activity, a model isbuilt by analyzing activities from a collection of patients from avariety of demographics, and then analyzing these data with astatistical approach, as described in detail below.

Life-Threatening Alarms/Alerts

ASY and VFIB are typically determined directly from the ECG waveformusing algorithms known in the art. To reduce false alarms associatedwith these events, the body-worn monitor calculates ASY and VFIB fromthe ECG waveform, and simultaneously determines a ‘significant pulse’from both the PPG waveform and cNIBP measurement, described below. Asignificant pulse occurs when the monitor detects a pulse rate from thePPG waveform (see, for example, 51 in FIG. 2) ranging from 30-150 bpm,and a pulse pressure separating SYS and DIA greater than 30 mmHg. WhenASY and VFIB are detected from the ECG waveform, the monitorcontinuously checks for a significant pulse and compares the patient'scurrent pulse rate to that measured during the entire previous 60seconds. The alarm/alert related to ASY and VFIB are delayed, typicallyby 10-20 seconds, if the pulse is significant and the pulse ratemeasured during this period differs from patient's current pulse rate byless than 40%. The monitor sounds an alarm/alert if ASY and VFIBmeasured from the ECG waveform persists after the delay period. Thealarm/alert is not generated if ASY and VFIB are no longer detectedafter the delay period.

The alarm/alert for ASY and VFIB additionally depends on the patient'sactivity level. For example, if the monitor determines ASY and VFIB fromthe ECG, and that the patient is walking from the ACC waveforms, it thenchecks for a significant pulse and determines pulse rate from the PPGwaveform. In this situation the patient is assumed to be in an activitystate prone to false alarms. The alarms/alerts related to ASY and VFIBare thus delayed, typically by 20-30 seconds, if the monitor determinesthe patient's pulse to be significant and their current pulse differsfrom their pulse rate measured during the previous 60 seconds by lessthan 40%. The monitor sounds an alarm only if ASY and VFIB remain afterthe delay period and once the patient stops walking. In anotherembodiment, an alarm/alert is immediately sounded if the monitor detectseither ASY or VFIB, and no significant pulse is detected from the PPGwaveform for between 5-10 seconds.

The methodology for alarms/alerts is slightly different for VTAC due tothe severity of this condition. VTAC, like ASY and VFIB, is detecteddirectly from the ECG waveform using algorithms know in the art. Thiscondition is typically defined as five or more consecutive prematureventricular contractions (PVCs) detected from the patient's ECG. WhenVTAC is detected from the ECG waveform, the monitor checks for asignificant pulse and compares the patient's current pulse rate to thatmeasured during the entire previous 60 seconds. The alarm/alert relatedto VTAC is delayed, typically by 20-30 seconds, if the pulse isdetermined to be significant and the pulse rate measured during thisperiod differs from patient's current pulse rate by less than 25%. Themonitor immediately sounds an alarm/alert if VTAC measured from the ECGwaveform meets the following criteria: 1) its persists after the delayperiod; 2) the deficit in the pulse rate increases to more than 25% atany point during the delay period; and 3) no significant pulse ismeasured for more than 8 consecutive seconds during the delay period.The alarm for VTAC is not generated if any of these criteria are notmet.

APNEA refers to a temporary suspension in a patient's breathing and isdetermined directly from respiratory rate. The monitor measures thisvital sign from the ECG waveform using techniques called ‘impedancepneumography’ or ‘impedance rheography’, both of which are known in theart. The monitor sounds an alarm/alert only if APNEA is detected andremains (i.e. the patient does not resume normal breathing) for a delayperiod of between 20-30 seconds.

The monitor does not sound an alarm/alert if it detects ASY, VFIB, VTAC,or APNEA from the ECG waveform and the patient is walking (orexperiencing a similar motion that, unlike falling or convulsing, doesnot result in an immediate alarm/alert). The monitor immediately soundsan alarm during both the presence and absence of these conditions if itdetects that the patient is falling, has fell and remains on the groundfor more than 10 seconds, or is having a Grand-mal seizure or similarconvulsion. These alarm criteria are similar to those described in theheuristic rules, above.

Threshold Alarms/Alerts

Threshold alarms are generated by comparing vital signs measured by thebody-worn monitor to fixed values that are either preprogrammed orcalculated in real time. These threshold values are separated, asdescribed below, into both outer limits (OL) and inner limits (IL). Thevalues for OL are separated into an upper outer limit (UOL) and a lowerouter limit (LOL). Default values for both UOL and LOL are typicallypreprogrammed into the body-worn monitor during manufacturing, and canbe adjusted by a medical professional once the monitor is deployed.Table 2, below, lists typical default values corresponding to each vitalsign for both UOL and LOL.

Values for IL are typically determined directly from the patient's vitalsigns. These values are separated into an upper inner limit (UIL) and alower inner limit (LIL), and are calculated from the UOL and LOL, anupper inner value (UIV), and a lower inner value (LIV). The UIV and LIVcan either be preprogrammed parameters (similar to the UOL and LOL,described above), or can be calculated directly from the patient's vitalsigns using a simple statistical process described below:UIL=UIV+(UOL−UIV)/3(option A): UIV→preset factory parameter adjusted by medicalprofessional(option B): UIV→1.3×weighted average of vital sign over previous 120 sLIL=LIV+(LOL−LIV)/3(option A): LIV→preset factory parameter adjusted by medicalprofessional(option B): LIV→0.7×weighted average of vital sign over previous 120 s

In a preferred embodiment the monitor only sounds an alarm/alert whenthe vital sign of issue surpasses the UOL/LOL and the UIL/LIL for apredetermined time period. Typically, the time periods for the UOL/LOLare shorter than those for the UIL/LIL, as alarm limits corresponding tothese extremities represent a relatively large deviation for normalvalues of the patient's vital signs, and are therefore considered to bemore severe. Typically the delay time periods for alarms/alertsassociated with all vital signs (other than temperature, which tends tobe significantly less labile) are 10 s for the UOL/LOL, and 120-180 sfor the UIL/LIL. For temperature, the delay time period for the UOL/LOLis typically 600 s, and the delay time period for the UIL/LIL istypically 300 s.

Other embodiments are also possible for the threshold alarms/alerts. Forexample, the body-worn monitor can sound alarms having different tonesand durations depending if the vital sign exceeds the UOL/LOL orUIL/LIL. Similarly, the tone can be escalated (in terms of acousticfrequency, alarm ‘beeps’ per second, and/or volume) depending on howlong, and by how much, these thresholds are exceeded. Alarms may alsosound due to failure of hardware within the body-worn monitor, or if themonitor detects that one of the sensors (e.g. optical sensor, ECGelectrodes) becomes detached from the patient.

TABLE 2 default alarm/alert values for UOL, UIV, LIV, and LOL Algorithmfor Generating Alarms/Alerts Default Default Default Default Upper OuterUpper Inner Lower Inner Lower Outer Vital Sign Limit (UOL) Value (UIV)Value (LIV) Limit (LOL) Blood Pressure (SYS) 180 mmHg 160 mmHg 90 mmHg80 mmHg Blood Pressure (MAP) 130 mmHg 120 mmHg 70 mmHg 60 mmHg BloodPressure (DIA) 120 mmHg 110 mmHg 60 mmHg 50 mmHg Heart Rate 150 bpm 135bpm 45 bpm 40 bpm Respiratory Rate 30 bmp 25 bmp 7 bpm 5 bpm SpO2 100%O2 90% O2 93% O2 85% O2 Temperature 103 deg. F. 101 deg. F. 95 deg. F.96.5 deg. F.

FIG. 8 shows a flow chart describing a high-level algorithm 85 forprocessing a patient's vital signs, along with their motion and activitylevel, to generate alarms/alerts for a hospitalized patient. It beginswith continuously measuring the patient's vital signs with the body-wornmonitor, optical sensor, and ECG electrodes, which are worn,respectively, on the patient's wrist, thumb, and chest (step 93).Simultaneously, three accelerometers associated with the monitor measuretime-dependent ACC waveforms from the patient's wrist, bicep, and chest(step 90). The algorithm 85 determines the frequency-dependent powerspectra of the ACC waveforms, and then analyzes the waveforms' temporaland frequency content (step 91). A statistical model, described indetail below, processes this information to determine patient's activitylevel, posture, and degree of motion (step 92). Once this information isdetermined, the algorithm processes it to generate a high percentage of‘true positive’ alarms/alerts for one or more hospitalized patients.This is done with a series of separate algorithmic modules 94, 95, 96,97 within the algorithm 85, with each module corresponding to adifferent activity state. Note that the algorithm 85 shown in FIG. 8includes four modules (corresponding to resting, walking, convulsing,and falling), but more could be added, presuming they could accuratelyidentify a specific activity state. Ultimately this depends how well aROC curve (similar to those shown below in FIGS. 19A, B) associated withthe specific activity state can predict the activity. The nature ofthese curves, in turn, depends on the uniqueness of activity-dependentfeatures in both the time-dependent ACC waveforms and their powerspectra. For example, the power spectra of ACC waveforms correspondingto a patient lying on their back will have essentially the same ACvalues compared to those measured when the patient is lying on theirside. However, due to the relative positioning of their limbs in thesetwo states, the DC values of the time-dependent ACC waveforms willdiffer. This means these two states can likely be distinguished. Incontrast, a patient brushing their teeth will exhibit bothtime-dependent ACC waveforms and associated power spectra that arevirtually identical to those of a patient having a Grand-mal seizure.For this reason these two activity states cannot likely bedistinguished.

The first module 94 corresponds to a resting patient. In this state, thepatient generates ECG, PPG, and ACC waveforms similar to those shown inFIG. 2. The module 94 processes motion and vital sign informationextracted from these waveforms to determine if the patient is indeedresting. If so, the module 94 uses the threshold alarm/alert criteriafor each vital sign described in Table 2. If the module 94 determinesthat the patient is not resting, the algorithm 85 progresses to the nextmodule 95, which analyzes the motion data to determine if the patient iswalking. If so, the module 95 uses the heuristic alarm/alert criteriadescribed in Table 1A, and if necessary generates an alarm/alert basedon the patient's vital signs (step 99). If the module 95 determines thatthe patient is not walking, the algorithm 85 progresses to the nextmodule 96, which determines if the patient is convulsing (e.g. having aGrand-mal seizure). If so, the module 95 uses the heuristic alarm/alertcriterion described in Table 1B (step 101). This criterion ignores anyalarm/alert threshold values, and automatically generates an alarm/alertbecause of the convulsing patient. Finally, if the module 95 determinesthat the patient is not convulsing, the algorithm 85 proceeds to thenext module 97, which determines if the patient is falling. If thepatient has fallen the algorithm uses the heuristic alarm/alertcriterion described in Table 1C, which, like step 101, ignores anythreshold values and automatically generates an alarm/alert (step 103).If the module 97 determines that the patient has not fallen, thealgorithm 85 does not generate any alarm/alert, and the process isrepeated, beginning with steps 90 and 93. In a typical embodiment, thealgorithm 85 is repeated every 10-20 seconds using computer codeoperating on the body-worn monitor.

Method for Displaying Alarms/Alerts Using Graphical User Interfaces

Graphical user interfaces (GUI) operating on both the body-worn moduleand the remote monitor can render graphical icons that clearly identifythe above-described patient activity states. FIG. 9 shows examples ofsuch icons 105 a-h, and Table 3, below, describes how they correspond tospecific patient activity states. As shown in FIGS. 10A, B and 11A, B,these icons are used in GUIs for both the body-worn monitor and remotemonitor.

TABLE 3 description of icons shown in FIG. 9 and used in GUIs for bothbody-worn monitor and remote monitor Icon Activity State 105a Standing105b Falling 105c resting; lying on side 105d Convulsing 105e Walking105f Sitting 105g resting; lying on stomach 105h resting; lying on back

FIGS. 10A and 10B show patient (106 in FIG. 10A) and map (107 in FIG.10B) views from a GUI typically rendered on a remote monitor, such as amonitoring station deployed at a central nursing station in thehospital. The remote monitor simultaneously communicates with multiplebody-worn monitors, each deployed on a patient in an area of thehospital (e.g. a bay of hospital beds, or an ED). The body-worn monitorscommunicate through an interface that typically includes both wirelessand wired components.

The patient view 106 is designed to give a medical professional, such asa nurse or doctor, a quick, easy-to-understand status of all thepatients of all the patients in the specific hospital area. In a singleglance the medical professional can determine their patients' vitalsigns, measured continuously by the body-worn monitor, along with theiractivity state and alarm status. The view 106 features a separate area108 corresponding to each patient. Each area 108 includes text fieldsdescribing the name of the patient and supervising clinician; numbersassociated with the patient's bed, room, and body-worn monitor; and thetype of alarm generated from the patient. Graphical icons, similar tothose shown in FIG. 9, indicate the patient's activity level. Additionalicons show the body-worn monitor's battery power, wireless signalstrength, and whether or not an alarm has been generated. Each area 108also clearly indicates numerical values for each vital sign measuredcontinuously by the body-worn monitor. The monitor displaying thepatient view 106 typically includes a touchpanel. Tapping on thepatient-specific area 108 generates a new view (not shown in the figure)that expands all the information in the area 108, and additionally showstime-dependent waveforms (similar to those shown in FIGS. 2-5)corresponding to the patient.

FIG. 10B shows a map view 107 that indicates the location and activitystate of each patient in the hospital area. Each patient's location istypically determined by processing the wireless signal from theirbody-worn monitor (e.g., by triangulating on signals received byneighboring 802.11 base stations, or simply using proximity to the basestation) or by using more advanced methods (e.g. time-of-flight analysisof the wireless signal, or conventional or network-assisted GPS), bothof which are done using techniques known in the art. The patient'slocation is mapped to a grid representing the distribution of beds inthe hospital area to generate the map view 107. The map view 107typically refreshes every 10-20 seconds, showing an updated location andactivity state for each patient.

FIGS. 11A and 11B show GUIs rendered by a display screen directly on thebody-worn monitor. The GUIs feature screens 125, 126 that are designedfor the patient (125 in FIG. 11A) and medical professional (126 in FIG.11B). The patient view 125 purposefully lacks any content related tovital signs, and instead is designed to be relatively generic, featuringthe time, date, and icons indicating the patient's activity level,whether or not an alarm has been generated, battery life, and wirelesssignal strength. The display screen is a touch panel, and features agraphical ‘call nurse’ button that, once depressed, sends a signal tothe central nursing station indicating that the patient needs assistancefrom a nurse. The patient view 125 includes a button labeled ‘UNLOCK’that, once activated, allows a nurse or doctor to activate the medicalprofessional view 126 shown in FIG. 11B. Tapping the UNLOCK buttonpowers an infrared barcode scanner in the body-worn monitor; this scansa barcode printed on a badge of the nurse of doctor and compares anencoded identifier to a database stored in an internal memory. A matchprompts the monitor to render the medical professional view 126, shownin FIG. 11B.

The medical professional view 126 is designed to have a look and feelsimilar to each area 108 shown in FIG. 10A. This makes it relativelyeasy for the nurse to interpret information rendered on both thebody-worn monitor and remote monitor. The view 126 features fields for apatient identifier, numerical values for vital signs, a time-dependentECG waveform with a span of approximately 5 seconds, and iconsindicating battery life, wireless signal strength, and whether or not analarm has been generated. A fixed bar proximal to the ECG waveformindicates a signal strength of 1 mV, as required by the AAMI:ANSI EC13specification for cardiac monitors. Depressing the ‘PATIENT VIEW’ buttoncauses the GUI to revert back to the patient view 125 shown in FIG. 11A.

Algorithms for Determining Patient Motion, Posture, Arm Height, ActivityLevel and the Effect of these Properties on Blood Pressure

Described below is an algorithm for using the three accelerometersfeatured in the above-described body-worn monitor to calculate apatient's motion, posture, arm height, activity level. Each of theseparameters affects both blood pressure and PTT, and thus inclusion ofthem in an algorithm can improve the accuracy of these measurements, andconsequently reduce false alarms/alerts associated with them.

Calculating Arm Height

To calculate a patient's arm height it is necessary to build amathematical model representing the geometric orientation of thepatient's arm, as detected with signals from the three accelerometers.FIG. 12 shows a schematic image of a coordinate system 129 centeredaround a plane 130 used to build this model for determining patientmotion and activity level, and arm height. Each of these parameters, asdiscussed in detail below, has an impact on the patient's vital signs,and particularly blood pressure.

The algorithm for estimating a patient's motion and activity levelbegins with a calculation to determine their arm height. This is doneusing signals from accelerometers attached to the patient's bicep (i.e.,with reference to FIG. 20A, an accelerometer included in the bulkheadportion 296 of cable 286) and wrist (i.e. the accelerometersurface-mounted to a circuit board within the wrist-worn transceiver272). The mathematical model used for this algorithm features acalibration procedure used to identify the alignment of an axisassociated with a vector R_(A), which extends along the patient's arm.Typically this is done by assuming the body-worn monitor is attached tothe patient's arm in a manner consistent with that that shown in FIGS.20A, B, and by using preprogrammed constants stored in memory associatedwith the CPU. Alternatively this can be done by prompting the patient(using, e.g., the wrist-worn transceiver) to assume a known andconsistent position with respect to gravity (e.g., hanging their armdown in a vertical configuration). The axis of their arm is determinedby sampling a DC portion of time-dependent ACC waveforms along the x, y,and z axes associated with the two above-mentioned accelerometers (i.e.ACC₁₋₆; the resultant values have units of g's) during the calibrationprocedure, and storing these numerical values as a vector in memoryaccessible with the CPU within the wrist-worn transceiver.

The algorithm determines a gravitational vector R_(GA) at a later timeby again sampling DC portions of ACC₁₋₆. Once this is complete, thealgorithm determines the angle _(GA) between the fixed arm vector R_(A)and the gravitational vector R_(GA) by calculating a dot product of thetwo vectors. As the patient moves their arm, signals measured by the twoaccelerometers vary, and are analyzed to determine a change in thegravitational vector R_(GA) and, subsequently, a change in the angle_(GA). The angle _(GA) can then be combined with an assumed, approximatelength of the patient's arm (typically 0.8 m) to determine its heightrelative to a proximal joint, e.g. the elbow.

FIG. 13 indicates how this model and approach can be extended todetermine the relative heights of the upper 137 and lower 136 segmentsof a patient's arm 135. In this derivation, described below, i, j, krepresent the vector directions of, respectively, the x, y, and z axesof the coordinate system 129 shown in FIG. 12. Three accelerometers 132a-c are disposed, respectively, on the patient's chest just above theirarmpit, near their bicep, and near their wrist; this is consistent withpositioning within the body-worn monitor, as described in FIGS. 20A,B.The vector R_(B) extending along the upper portion 137 of the patient'sarm is defined in this coordinate system as:{right arrow over (R)} _(B) =r _(Bx) î+r _(By) ĵ+r _(Bz) {circumflexover (k)}  (1)At any given time, the gravitational vector R_(GB) is determined fromACC waveforms (ACC₁₋₃) using signals from the accelerometer 132 blocated near the patient's bicep, and is represented by equation (2)below:{right arrow over (R)} _(GB)[n]=y _(Bx)[n]î+y _(By)[n]ĵ+y_(Bz)[n]{circumflex over (k)}  (2)Specifically, the CPU in the wrist-worn transceiver receives digitizedsignals representing the DC portion of the ACC₁₋₃ signals measured withaccelerometer 132 b, as represented by equation (3) below, where theparameter n is the value (having units of g's) sampled directly from theDC portion of the ACC waveform:y _(Bx)[n]=y _(DC,Bicep,x)[n];y _(By)[n]=y _(DC,Bicep,y)[n];y _(Bz) =y_(DC,Bicep,z)[n]  (3)The cosine of the angle _(GB) separating the vector R_(B) and thegravitational vector R_(GB) is determined using equation (4):

$\begin{matrix}{{\cos\left( {\theta_{GB}\lbrack n\rbrack} \right)} = \frac{{{\overset{\rightharpoonup}{R}}_{GB}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{B}}{{{{\overset{\rightharpoonup}{R}}_{GB}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{B}}}} & (4)\end{matrix}$The definition of the dot product of the two vectors R_(B) and R_(GB)is:{right arrow over (R)} _(GB)[n]·{right arrow over (R)} _(B)=(y_(Bx)[n]×r _(Bx))+(y _(By)[n]×r _(By))+(y _(Bz)[n]×r _(Bz))  (5)and the definitions of the norms or magnitude of the vectors R_(B) andR_(GB) are:∥{right arrow over (R)} _(GB)[n]∥=√{square root over ((y _(Bx)[n])²+(y_(By)[n])²+(y _(Bz)[n])²)}  (6)and∥{right arrow over (R)} _(B)∥=√{square root over ((r _(Bx))²+(r_(By))²+(r _(Bz))²)}  (7)Using the norm values for these vectors and the angle _(GB) separatingthem, as defined in equation (4), the height of the patient's elbowrelative to their shoulder joint, as characterized by the accelerometeron their chest (h_(E)), is determined using equation (8), where thelength of the upper arm is estimated as L_(B):h _(E)[n]=−L _(B)×cos(θ_(GB)[n])  (8)As is described in more detail below, equation (8) estimates the heightof the patient's arm relative to their heart. And this, in turn, can beused to further improve the accuracy of PTT-based blood pressuremeasurements.

The height of the patient's wrist joint h_(W) is calculated in a similarmanner using DC components from the time-domain waveforms (ACC₄₋₆)collected from the accelerometer 132 a mounted within the wrist-worntransceiver. Specifically, the wrist vector R_(W) is given by equation(9):{right arrow over (R)} _(W) =r _(Wx) î+r _(Wy) ĵ+r _(Wz) {circumflexover (k)}  (9)and the corresponding gravitational vector R_(GW) is given by equation(10):{right arrow over (R)} _(GW)[n]=y _(Wx)[n]î+y _(Wy)[n]ĵ+y_(Wz)[n]{circumflex over (k)}  (10)The specific values used in equation (10) are measured directly from theaccelerometer 132 a; they are represented as n and have units of g's, asdefined below:y _(Wx)[n]=y _(DC,Wrist,x)[n];y _(Wy)[n]=y _(DC,Wrist,y)[n];y _(Wz)[n]=y_(DC,Wrist,z)[n]  (11)The vectors R_(W) and R_(GW) described above are used to determine thecosine of the angle _(GW) separating them using equation (12):

$\begin{matrix}{{\cos\left( {\theta_{GW}\lbrack n\rbrack} \right)} = \frac{{{\overset{\rightharpoonup}{R}}_{GW}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{W}}{{{{\overset{\rightharpoonup}{R}}_{WB}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{W}}}} & (12)\end{matrix}$The definition of the dot product between the vectors R_(W) and R_(GW)is:{right arrow over (R)} _(GW)[n]·{right arrow over (R)} _(W)=(y_(Wx)[n]×r _(Wx))+(y _(Wy)[n]×r _(Wy))+(y _(Wz)[n]×r _(Wz))  (13)and the definitions of the norm or magnitude of both the vectors R_(W)and R_(GW) are:∥{right arrow over (R)} _(GW)[n]∥=√{square root over ((y _(Wx)[n])²+(y_(Wy)[n])²+(y _(Wz)[n])²)}  (14)and∥{right arrow over (R)} _(W)∥=√{square root over ((r _(Wx))²+(r_(Wy))²+(r _(Wz))²)}  (15)The height of the patient's wrist h_(W) can be calculated using the normvalues described above in equations (14) and (15), the cosine valuedescribed in equation (12), and the height of the patient's elbowdetermined in equation (8):h _(W)[n]=h _(E)[n]−L _(W)×cos(θ_(GW)[n])  (16)In summary, the algorithm can use digitized signals from theaccelerometers mounted on the patient's bicep and wrist, along withequations (8) and (16), to accurately determine the patient's arm heightand position. As described below, these parameters can then be used tocorrect the PTT and provide a blood pressure calibration, similar to thecuff-based indexing measurement described above, that can furtherimprove the accuracy of this measurement.Calculating the Influence of Arm Height on Blood Pressure

A patient's blood pressure, as measured near the brachial artery, willvary with their arm height due to hydrostatic forces and gravity. Thisrelationship between arm height and blood pressure enables twomeasurements: 1) a blood pressure ‘correction factor’, determined fromslight changes in the patient's arm height, can be calculated and usedto improve accuracy of the base blood pressure measurement; and 2) therelationship between PTT and blood pressure can be determined (like itis currently done using the indexing measurement) by measuring PTT atdifferent arm heights, and calculating the change in PTT correspondingto the resultant change in height-dependent blood pressure.Specifically, using equations (8) and (16) above, and (21) below, analgorithm can calculate a change in a patient's blood pressure (BP)simply by using data from two accelerometers disposed on the wrist andbicep. The BP can be used as the correction factor. Exact blood pressurevalues can be estimated directly from arm height using an initial bloodpressure value (determined, e.g., using the cuff-based module during aninitial indexing measurement), the relative change in arm height, andthe correction factor. This measurement can be performed, for example,when the patient is first admitted to the hospital. PTT determined atdifferent arm heights provides multiple data points, each correspondingto a unique pair of blood pressure values determined as described above.The change in PTT values (PTT) corresponds to changes in arm height.

From these data, the algorithm can calculate for each patient how bloodpressure changes with PTT, i.e. BP/PTT. This relationship relates tofeatures of the patient's cardiovascular system, and will evolve overtime due to changes, e.g., in the patient's arterial tone and vascularcompliance. Accuracy of the body-worn monitor's blood pressuremeasurement can therefore be improved by periodically calculatingBP/PTT. This is best done by: 1) combining a cuff-based initial indexingmeasurement to set baseline values for SYS, DIA, and MAP, and thendetermining BP/PTT as described above; and 2) continually calculatingBP/PTT by using the patient's natural motion, or alternatively usingwell-defined motions (e.g., raising and lower the arm to specificpositions) as prompted at specific times by monitor's user interface.

Going forward, the body-worn monitor measures PTT, and can use thisvalue and the relationship determined from the above-describedcalibration to convert this to blood pressure. All future indexingmeasurements can be performed on command (e.g., using audio or visualinstructions delivered by the wrist-worn transceiver) using changes inarm height, or as the patient naturally raises and lowers their arm asthey move about the hospital.

To determine the relationship between PTT, arm height, and bloodpressure, the algorithm running on the wrist-worn transceiver is derivedfrom a standard linear model shown in equation (17):

$\begin{matrix}{{PTT} = {{\left( \frac{1}{m_{BP}} \right) \times P_{MAP}} + \overset{\sim}{B}}} & (17)\end{matrix}$Assuming a constant velocity of the arterial pulse along an arterialpathway (e.g., the pathway extending from the heart, through the arm, tothe base of the thumb):

$\begin{matrix}{\frac{\partial({PWV})}{\partial r} = 0} & (18)\end{matrix}$the linear PTT model described in equation (17) becomes:

$\begin{matrix}{\frac{\partial({PTT})}{\partial r} = {\left( \frac{1}{L} \right)\left( {{\frac{1}{m_{BP}} \times {MAP}} + \overset{\sim}{B}} \right)}} & (19)\end{matrix}$Equation (19) can be solved using piecewise integration along the upper137 and lower 136 segments of the arm to yield the following equationfor height-dependent PTT:

$\begin{matrix}{{PTT} = {\left( {{\frac{1}{m_{BP}} \times {MAP}} + B} \right) - {\frac{1}{m_{BP}} \times \begin{bmatrix}{{\left( \frac{L_{1}}{L} \right)\left( \frac{\rho\;{Gh}_{E}}{2} \right)} +} \\{\left( \frac{L_{2}}{L} \right)\left( {\frac{\rho\; G}{2}\left( {h_{w} + h_{E}} \right)} \right)}\end{bmatrix}}}} & (20)\end{matrix}$From equation (20) it is possible to determine a relative pressurechange P_(rel) induced in a cNIBP measurement using the height of thepatient's wrist (h_(W)) and elbow (h_(E)):

$\begin{matrix}{{P_{rel}\lbrack n\rbrack} = {{\left( \frac{L_{1}}{L} \right)\left( \frac{\rho\;{{Gh}_{E}\lbrack n\rbrack}}{2} \right)} + {\left( \frac{L_{2}}{L} \right)\left( {\frac{\rho\; G}{2}\left( {{h_{w}\lbrack n\rbrack} + {h_{E}\lbrack n\rbrack}} \right)} \right)}}} & (21)\end{matrix}$As described above, P_(rel) can be used to both calibrate the cNIBPmeasurement deployed by the body-worn monitor, or supply aheight-dependent correction factor that reduces or eliminates the effectof posture and arm height on a PTT-based blood pressure measurement.

FIG. 14 shows actual experimental data that illustrate how PTT changeswith arm height. Data for this experiment were collected as the subjectperiodically raised and lowered their arm using a body-worn monitorsimilar to that shown in FIGS. 20A and 20B. Such motion would occur, forexample, if the patient was walking. As shown in FIG. 14, changes in thepatient's elbow height are represented by time-dependent changes in theDC portion of an ACC waveform, indicated by trace 160. These data aremeasured directly from an accelerometer positioned near the patient'sbicep, as described above. PTT is measured from the same arm using thePPG and ECG waveforms, and is indicated by trace 162. As the patientraises and lowers their arm their PTT rises and falls accordingly,albeit with some delay due to the reaction time of the patient'scardiovascular system.

Calculating a Patient's Posture

As described above in Tables 1A-C, a patient's posture can influence howthe above-described system generates alarms/alerts. The body-wornmonitor can determine a patient's posture using time-dependent ACCwaveforms continuously generated from the three patient-wornaccelerometers, as shown in FIGS. 20A, B. In embodiments, theaccelerometer worn on the patient's chest can be exclusively used tosimplify this calculation. An algorithm operating on the wrist-worntransceiver extracts DC values from waveforms measured from thisaccelerometer and processes them with an algorithm described below todetermine posture. Specifically, referring to FIG. 15, torso posture isdetermined for a patient 145 using angles determined between themeasured gravitational vector and the axes of a torso coordinate space146. The axes of this space 146 are defined in a three-dimensionalEuclidean space where {right arrow over (R)}_(CV) is the vertical axis,{right arrow over (R)}_(CH) is the horizontal axis, and {right arrowover (R)}_(CN) is the normal axis. These axes must be identifiedrelative to a ‘chest accelerometer coordinate space’ before thepatient's posture can be determined.

The first step in this procedure is to identify alignment of {rightarrow over (R)}_(CV) in the chest accelerometer coordinate space. Thiscan be determined in either of two approaches. In the first approach,{right arrow over (R)}_(CV) is assumed based on a typical alignment ofthe body-worn monitor relative to the patient. During manufacturing,these parameters are then preprogrammed into firmware operating on thewrist-worn transceiver. In this procedure it is assumed thataccelerometers within the body-worn monitor are applied to each patientwith essentially the same configuration. In the second approach, {rightarrow over (R)}_(CV) is identified on a patient-specific basis. Here, analgorithm operating on the wrist-worn transceiver prompts the patient(using, e.g., video instruction operating on the display, or audioinstructions transmitted through the speaker) to assume a known positionwith respect to gravity (e.g., standing up with arms pointed straightdown). The algorithm then calculates {right arrow over (R)}_(CV) from DCvalues corresponding to the x, y, and z axes of the chest accelerometerwhile the patient is in this position. This case, however, stillrequires knowledge of which arm (left or right) the monitor is worn on,as the chest accelerometer coordinate space can be rotated by 180degrees depending on this orientation. A medical professional applyingthe monitor can enter this information using the GUI, described above.This potential for dual-arm attachment requires a set of twopre-determined vertical and normal vectors which are interchangeabledepending on the monitor's location. Instead of manually entering thisinformation, the arm on which the monitor is worn can be easilydetermined following attachment using measured values from the chestaccelerometer values, with the assumption that {right arrow over(R)}_(CV) is not orthogonal to the gravity vector.

The second step in the procedure is to identify the alignment of {rightarrow over (R)}_(CN) in the chest accelerometer coordinate space. Themonitor can determine this vector, similar to the way it determines{right arrow over (R)}_(CV), with one of two approaches. In the firstapproach the monitor assumes a typical alignment of the chest-wornaccelerometer on the patient. In the second approach, the alignment isidentified by prompting the patient to assume a known position withrespect to gravity. The monitor then calculates {right arrow over(R)}_(CN) from the DC values of the time-dependent ACC waveform.

The third step in the procedure is to identify the alignment of {rightarrow over (R)}_(CH) in the chest accelerometer coordinate space. Thisvector is typically determined from the vector cross product of {rightarrow over (R)}_(CV) and {right arrow over (R)}_(CN), or it can beassumed based on the typical alignment of the accelerometer on thepatient, as described above.

FIG. 16 shows the geometrical relationship between {right arrow over(R)}_(CV) 140, {right arrow over (R)}_(CN) 141, and {right arrow over(R)}_(CH) 142 and a gravitational vector {right arrow over (R)}_(G) 143measured from a moving patient in a chest accelerometer coordinate space139. The body-worn monitor continually determines a patient's posturefrom the angles separating these vectors. Specifically, the monitorcontinually calculates {right arrow over (R)}_(G) 143 for the patientusing DC values from the ACC waveform measured by the chestaccelerometer. From this vector, the body-worn monitor identifies angles(θ_(VG), θ_(NG), and θ_(HG)) separating it from {right arrow over(R)}_(CV) 140, {right arrow over (R)}_(CN) 141, and {right arrow over(R)}_(CH) 142. The body-worn monitor then compares these three angles toa set of predetermine posture thresholds to classify the patient'sposture.

The derivation of this algorithm is as follows. Based on either anassumed orientation or a patient-specific calibration proceduredescribed above, the alignment of {right arrow over (R)}_(CV) in thechest accelerometer coordinate space is given by:{right arrow over (R)} _(CV) =r _(CVx) î+r _(CVy) ĵ+r _(CVz) {circumflexover (k)}  (22)At any given moment, {right arrow over (R)}_(G) is constructed from DCvalues of the ACC waveform from the chest accelerometer along the x, y,and z axes:{right arrow over (R)} _(G)[n]=y _(Cx)[n]î+y _(Cy)[n]ĵ+y_(Cz)[n]{circumflex over (k)}  (23)Equation (24) shows specific components of the ACC waveform used forthis calculation:y _(Cx)[n]=y _(DC,chest,x)[n];y _(Cy)[n]=y _(DC,chest,y)[n];y _(Cz)[n]=y_(DC,chest,z)[n]  (24)The angle between {right arrow over (R)}_(CV) and {right arrow over(R)}_(G) is given by equation (25):

$\begin{matrix}{{\theta_{VG}\lbrack n\rbrack} = {\arccos\left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{CV}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{CV}}} \right)}} & (25)\end{matrix}$where the dot product of the two vectors is defined as:{right arrow over (R)} _(G)[n]·{right arrow over (R)} _(CV)=(y_(Cx)[n]×r _(CVx))+(y _(Cy)[n]×r _(CVy))+(y _(Cz)[n]×r _(CVz))  (26)The definition of the norms of {right arrow over (R)}_(G) and {rightarrow over (R)}_(CV) are given by equations (27) and (28):∥{right arrow over (R)} _(G)[n]∥=√{square root over ((y _(Cx)[n])²+(y_(Cy)[n])²+(y _(Cz)[n])²)}  (27)∥{right arrow over (R)} _(CV)∥=√{square root over ((r _(CVx))²+(r_(CVy))²+(r _(CVz))²)}  (28)

As shown in equation (29), the monitor compares the vertical angleθ_(VG) to a threshold angle to determine whether the patient is vertical(i.e. standing upright) or lying down:if θ_(VG)≤45° then Torso State=0, the patient is upright  (29)If the condition in equation (29) is met the patient is assumed to beupright, and their torso state, which is a numerical value equated tothe patient's posture, is equal to 0. The torso state is processed bythe body-worn monitor to indicate, e.g., a specific icon correspondingto this state, such as icon 105 a in FIG. 9. The patient is assumed tobe lying down if the condition in equation (8) is not met, i.e.θ_(VG)>45 degrees. Their lying position is then determined from anglesseparating the two remaining vectors, as defined below.

The angle θ_(NG) between {right arrow over (R)}_(CN) and {right arrowover (R)}_(G) determines if the patient is lying in the supine position(chest up), prone position (chest down), or on their side. Based oneither an assumed orientation or a patient-specific calibrationprocedure, as described above, the alignment of {right arrow over(R)}_(CN) is given by equation (30), where i, j, k represent the unitvectors of the x, y, and z axes of the chest accelerometer coordinatespace respectively:{right arrow over (R)} _(CN) =r _(CNx) î+r _(CNy) ĵ+r _(CNz) {circumflexover (k)}  (30)The angle between {right arrow over (R)}_(CN) and {right arrow over(R)}_(G) determined from DC values extracted from the chestaccelerometer ACC waveform is given by equation (31):

$\begin{matrix}{{\theta_{NG}\lbrack n\rbrack} = {\arccos\left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{CN}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{CN}}} \right)}} & (31)\end{matrix}$The body-worn monitor determines the normal angle θ_(NG) and thencompares it to a set of predetermined threshold angles to determinewhich position the patient is lying in, as shown in equation (32):if θ_(NG)≤35° then Torso State=1, the patient is supineif θ_(NG)≥135° then Torso State=2, the patient is prone  (32)Icons corresponding to these torso states are shown, for example, asicons 105 h and 105 g in FIG. 9. If the conditions in equation (32) arenot met then the patient is assumed to be lying on their side. Whetherthey are lying on their right or left side is determined from the anglecalculated between the horizontal torso vector and measuredgravitational vectors, as described above.

The alignment of {right arrow over (R)}_(CH) is determined using eitheran assumed orientation, or from the vector cross-product of {right arrowover (R)}_(CV) and {right arrow over (R)}_(CN) as given by equation(33), where i, j, k represent the unit vectors of the x, y, and z axesof the accelerometer coordinate space respectively. Note that theorientation of the calculated vector is dependent on the order of thevectors in the operation. The order below defines the horizontal axis aspositive towards the right side of the patient's body.{right arrow over (R)} _(CH) =r _(CVx) î+r _(CVy) ĵ+r _(CVz) {circumflexover (k)}={right arrow over (R)} _(CV) ×{right arrow over (R)}_(CN)  (33)The angle θ_(HG) between {right arrow over (R)}_(CH) and {right arrowover (R)}_(G) is determined using equation (34):

$\begin{matrix}{{\theta_{HG}\lbrack n\rbrack} = {\arccos\left( \frac{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack} \cdot {\overset{\rightharpoonup}{R}}_{CH}}{{{{\overset{\rightharpoonup}{R}}_{G}\lbrack n\rbrack}}{{\overset{\rightharpoonup}{R}}_{CH}}} \right)}} & (34)\end{matrix}$The monitor compares this angle to a set of predetermined thresholdangles to determine if the patient is lying on their right or left side,as given by equation (35):if θ_(HG)≥90° then Torso State=3, the patient is on their right sideif θ_(NG)<90° then Torso State=4, the patient is on their leftside  (35)Table 4 describes each of the above-described postures, along with acorresponding numerical torso state used to render, e.g., a particularicon:

TABLE 4 postures and their corresponding torso states Posture TorsoState Upright 0 supine: lying on back 1 prone: lying on chest 2 lying onright side 3 lying on left side 4 undetermined posture 5FIGS. 17A and 17B show, respectively, graphs of time-dependent ACCwaveforms 150 measured along the x, y, and z-axes, and the torso states(i.e. postures) 151 determined from these waveforms for a movingpatient. As the patient moves, the DC values of the ACC waveformsmeasured by the chest accelerometer vary accordingly, as shown by thegraph 150 in FIG. 17A. The body-worn monitor processes these values asdescribed above to continually determine {right arrow over (R)}_(G) andthe various quantized torso states for the patient, as shown in thegraph 151 in FIG. 17B. The torso states yield the patient's posture asdefined in Table 4. For this study the patient rapidly alternatedbetween standing, lying on their back, chest, right side, and left sidewithin about 150 seconds. As described above, different alarm/alertconditions (e.g. threshold values) for vital signs can be assigned toeach of these postures, or the specific posture itself may result in analarm/alert. Additionally, the time-dependent properties of the graph151 can be analyzed (e.g. changes in the torso states can be counted) todetermine, for example, how often the patient moves in their hospitalbed. This number can then be equated to various metrics, such as a ‘bedsore index’ indicating a patient that is so stationary in their bed thatlesions may result. Such a state could then be used to trigger analarm/alert to the supervising medical professional.Calculating a Patient's Activity

An algorithm can process information generated by the accelerometersdescribed above to determine a patient's specific activity (e.g.,walking, resting, convulsing), which is then used to reduce theoccurrence of false alarms. This classification is done using a‘logistic regression model classifier’, which is a type of classifierthat processes continuous data values and maps them to an output thatlies on the interval between 0 and 1. A classification ‘threshold’ isthen set as a fractional value within this interval. If the model outputis greater than or equal to this threshold, the classification isdeclared ‘true’, and a specific activity state can be assumed for thepatient. If the model output falls below the threshold, then thespecific activity is assumed not to take place.

This type of classification model offers several advantages. First, itprovides the ability to combine multiple input variables into a singlemodel, and map them to a single probability ranging between 0 and 1.Second, the threshold that allows the maximum true positive outcomes andthe minimum false positive outcomes can be easily determined from a ROCcurve, which in turn can be determined using empirical experimentationand data. Third, this technique requires minimal computation.

The formula for the logistic regression model is given by equation (36)and is used to determine the outcome, P, for a set of buffered data:

$\begin{matrix}{P = \frac{1}{1 - {\exp\left( {- z} \right)}}} & (36)\end{matrix}$The logit variable z is defined in terms of a series of predictors(x_(i)), each affected by a specific type of activity, and determined bythe three accelerometers worn by the patient, as shown in equation (37):z=b ₀ +b ₁ x ₁ +b ₂ x ₂ + . . . +b _(m) x _(m)  (37)In this model, the regression coefficients (b_(i), i=0, 1, . . . , m)and the threshold (P_(th)) used in the patient motion classifier andsignal corruption classifiers are determined empirically from datacollected on actual subjects. The classifier results in a positiveoutcome as given in equation (38) if the logistic model output, P, isgreater than the predetermined threshold, P_(th):If P≥P _(th) then Classifier State=1  (38)

FIG. 18 shows a block diagram 180 indicating the mathematical model usedto determine the above-described logistic regression model classifier.In this model, the series of predictor variables (x_(i)) are determinedfrom statistical properties of the time-dependent ACC waveforms, alongwith specific frequency components contained in the power spectra ofthese waveforms. The frequency components are determined in alow-frequency region (0-20 Hz) of these spectra that corresponds tohuman motion, and are shown, for example, by the series of bars 81 inFIG. 7. Specifically, the predictor variables can be categorized byfirst taking a power spectrum of a time-dependent ACC waveform generatedby an accelerometer, normalizing it, and then separating the fractionalpower into frequency bands according to Table 5, below:

TABLE 5 predictor variables and their relationship to the accelerometersignal Predictor variable Description x₁ normalized power of the ACcomponent of the time- dependent accelerometer signal x₂ average armangle measured while time-dependent accelerometer signal is collected x₃standard deviation of the arm angle while time- dependent accelerometersignal is collected x₄ fractional power of the AC component of thefrequency-dependent accelerometer signal between 0.5-1.0 Hz x₅fractional power of the AC component of the frequency-dependentaccelerometer signal between 1.0-2.0 Hz x₆ fractional power of the ACcomponent of the frequency-dependent accelerometer signal between2.0-3.0 Hz x₇ fractional power of the AC component of thefrequency-dependent accelerometer signal between 3.0-4.0 Hz x₈fractional power of the AC component of the frequency-dependentaccelerometer signal between 4.0-5.0 Hz x₉ fractional power of the ACcomponent of the frequency-dependent accelerometer signal between5.0-6.0 Hz x₁₀ fractional power of the AC component of thefrequency-dependent accelerometer signal between 6.0-7.0 HzThe predictor variables described in Table 5 are typically determinedfrom ACC signals generated by accelerometers deployed in locations thatare most affected by patient motion. Such accelerometers are typicallymounted directly on the wrist-worn transceiver, and on the bulkheadconnector attached to the patient's arm. The normalized signal power(x₁) for the AC components (y_(W,i), i=x, y, z) calculated from the ACCis shown in equation (39), where F_(s) denotes the signal samplingfrequency, N is the size of the data buffer, and x_(norm) is apredetermined power value:

$\begin{matrix}{x_{1} = {\frac{1}{x_{norm}}\left( \frac{F_{s}}{N} \right){\sum\limits_{n = 1}^{N}\left\lbrack {\left( {y_{W,x}\lbrack n\rbrack} \right)^{2} + \left( {y_{W,y}\lbrack n\rbrack} \right)^{2} + \left( {y_{W,z}\lbrack n\rbrack} \right)^{2}} \right\rbrack}}} & (39)\end{matrix}$The average arm angle predictor value (x₂) was determined using equation(40):

$\begin{matrix}{x_{2} = {\left( \frac{1}{N} \right){\sum\limits_{n = 1}^{N}{\cos\left( {\theta_{GW}\lbrack n\rbrack} \right)}}}} & (40)\end{matrix}$Note that, for this predictor value, it is unnecessary to explicitlydetermine the angle _(GW) using an arccosine function, and the readilyavailable cosine value calculated in equation (12) acts as a surrogateparameter indicating the mean arm angle. The predictor value indicatingthe standard deviation of the arm angle (x₃) was determined usingequation (41) using the same assumptions for the angle _(GW) asdescribed above:

$\begin{matrix}{x_{3} = \sqrt{\left( \frac{1}{N} \right){\sum\limits_{n = 1}^{N}\left( {{\cos\left( {\theta_{GW}\lbrack n\rbrack} \right)} - x_{2}} \right)^{2}}}} & (41)\end{matrix}$

The remaining predictor variables (x₄-x₁₀) are determined from thefrequency content of the patient's motion, determined from the powerspectrum of the time-dependent accelerometer signals, as indicated inFIG. 7. To simplify implementation of this methodology, it is typicallyonly necessary to process a single channel of the ACC waveform.Typically, the single channel that is most affected by patient motion isy_(W), which represents motion along the long axis of the patient'slower arm, determined from the accelerometer mounted directly in thewrist-worn transceiver. Determining the power requires taking an N-pointFast Fourier Transform (FFT) of the accelerometer data (X_(W)[m]); asample FFT data point is indicated by equation (42):X _(W)[m]=a _(m) +ib _(m)  (42)Once the FFT is determined from the entire time-domain ACC waveform, thefractional power in the designated frequency band is given by equation(43), which is based on Parseval's theorem. The term mStart refers tothe FFT coefficient index at the start of the frequency band ofinterest, and the term mEnd refers to the FFT coefficient index at theend of the frequency band of interest:

$\begin{matrix}{x_{k} = {\left( \frac{1}{P_{T}} \right){\sum\limits_{m = {mStart}}^{mEnd}{\left( {a_{m} + {ib}_{m}} \right)\left( {a_{m} - {ib}_{m}} \right)}}}} & (43)\end{matrix}$Finally, the formula for the total signal power, P_(T), is given inequation (44):

$\begin{matrix}{P_{T} = {\sum\limits_{m = 0}^{N/2}{\left( {a_{m} + {ib}_{m}} \right)\left( {a_{m} - {ib}_{m}} \right)}}} & (44)\end{matrix}$

As described above, to accurately estimate a patient's activity level,predictor values x₁-x₁₀ defined above are measured from a variety ofsubjects selected from a range of demographic criteria (e.g., age,gender, height, weight), and then processed using predeterminedregression coefficients (b_(j)) to calculate a logit variable (definedin equation (37)) and the corresponding probability outcome (defined inequation (36)). A threshold value is then determined empirically from anROC curve. The classification is declared true if the model output isgreater than or equal to the threshold value. During an actualmeasurement, an accelerometer signal is measured and then processed asdescribed above to determine the predictor values. These parameters areused to determine the logit and corresponding probability, which is thencompared to a threshold value to estimate the patient's activity level.

FIGS. 19A,B show actual ROC curves, determined using accelerometersplaced on the upper and lower arms of a collection of patients. An idealROC curve indicates a high true positive rate (shown on the y-axis) anda low false positive rate (shown on the x-axis), and thus has a shapeclosely representing a 90 deg. angle. From such a curve a relativelyhigh threshold can be easily determined and used as described above todetermine a patient's activity level. Ultimately this results in ameasurement that yields a high percentage of ‘true positives’, and a lowpercentage of ‘false positives’. FIG. 19A shows, for example, a ROCcurve generated from the patients' upper 192 and lower 190 arms duringwalking. Data points on the curves 190, 192 were generated withaccelerometers and processed with algorithms as described above. Thedistribution of these data indicates that this approach yields a highselectivity for determining whether or not a patient is walking.

FIG. 19B shows data measured during resting. The ACC waveforms measuredfor this activity state feature fewer well-defined frequency componentscompared to those measured for FIG. 19A, mostly because the act of‘resting’ is not as well defined as that of ‘walking’. That is why theROC curves measured from the upper 194 and lower 196 arms have less ofan idealized shape. Still, from these data threshold values can bedetermined that can be used for future measurements to accuratelycharacterize whether or not the patient is resting.

ROC curves similar to those shown in FIGS. 19A, B can be generatedempirically from a set of patients undergoing a variety of differentactivity states. These states include, among others, falling,convulsing, running, eating, and undergoing a bowel movement. Athreshold value for each activity state is determined once the ROC curveis generated, and going forward this information can be incorporated inan algorithm for estimating the patient's activity. Such an algorithm,e.g., can be uploaded wirelessly to the wrist-worn transceiver.

Hardware System for Body-Worn Monitor

FIGS. 20A and 20B show how the body-worn monitor 10 described aboveattaches to a patient 270. These figures show two configurations of thesystem: FIG. 20A shows the system used during the indexing portion ofthe composite technique, and includes a pneumatic, cuff-based system285, while FIG. 20B shows the system used for subsequent cNIBPmeasurements. The indexing measurement typically takes about 60 seconds,and is typically performed once every 4 hours. Once the indexingmeasurement is complete the cuff-based system 285 is typically removedfrom the patient. The remainder of the time the system 10 performs thecNIBP measurement.

The body-worn monitor 10 features a wrist-worn transceiver 272,described in more detail in FIG. 21, featuring a touch panel interface273 that displays blood pressure values and other vital signs. FIGS.11A,B show examples of the touchpanel interface 273. A wrist strap 290affixes the transceiver 272 to the patient's wrist like a conventionalwristwatch. A cable 292 connects an optical sensor 294 that wraps aroundthe base of the patient's thumb to the transceiver 272. During ameasurement, the optical sensor 294 generates a time-dependent PPG whichis processed along with an ECG to measure blood pressure. PTT-basedmeasurements made from the thumb yield excellent correlation to bloodpressure measured with a femoral arterial line. This provides anaccurate representation of blood pressure in the central regions of thepatient's body.

To determine ACC waveforms the body-worn monitor 10 features threeseparate accelerometers located at different portions on the patient'sarm. The first accelerometer is surface-mounted on a circuit board inthe wrist-worn transceiver 272 and measures signals associated withmovement of the patient's wrist. The second accelerometer is included ina small bulkhead portion 296 included along the span of the cable 286.During a measurement, a small piece of disposable tape, similar in sizeto a conventional bandaid, affixes the bulkhead portion 296 to thepatient's arm. In this way the bulkhead portion 296 serves twopurposes: 1) it measures a time-dependent ACC waveform from themid-portion of the patient's arm, thereby allowing their posture and armheight to be determined as described in detail above; and 2) it securesthe cable 286 to the patient's arm to increase comfort and performanceof the body-worn monitor 10, particularly when the patient isambulatory.

The cuff-based module 285 features a pneumatic system 276 that includesa pump, valve, pressure fittings, pressure sensor, analog-to-digitalconverter, microcontroller, and rechargeable battery. During an indexingmeasurement, it inflates a disposable cuff 284 and performs twomeasurements according to the composite technique: 1) it performs aninflation-based measurement of oscillometry to determine values for SYS,DIA, and MAP; and 2) it determines a patient-specific relationshipbetween PTT and MAP. These measurements are performed according to thecomposite technique, and are described in detail in the above-referencedpatent application entitled: ‘VITAL SIGN MONITOR FOR MEASURING BLOODPRESSURE USING OPTICAL, ELECTRICAL, AND PRESSURE WAVEFORMS’ (U.S. Ser.No. 12/138,194; filed Jun. 12, 2008), the contents of which have beenpreviously incorporated herein by reference.

The cuff 284 within the cuff-based pneumatic system 285 is typicallydisposable and features an internal, airtight bladder that wraps aroundthe patient's bicep to deliver a uniform pressure field. During theindexing measurement, pressure values are digitized by the internalanalog-to-digital converter, and sent through a cable 286 according tothe CAN protocol, along with SYS, DIA, and MAP blood pressures, to thewrist-worn transceiver 272 for processing as described above. Once thecuff-based measurement is complete, the cuff-based module 285 is removedfrom the patient's arm and the cable 286 is disconnected from thewrist-worn transceiver 272. cNIBP is then determined using PTT, asdescribed in detail above.

To determine an ECG, the body-worn monitor 10 features a small-scale,three-lead ECG circuit integrated directly into a bulkhead 274 thatterminates an ECG cable 282. The ECG circuit features an integratedcircuit that collects electrical signals from three chest-worn ECGelectrodes 278 a-c connected through cables 280 a-c. The ECG electrodes278 a-c are typically disposed in a conventional ‘Einthoven's Triangle’configuration which is a triangle-like orientation of the electrodes 278a-c on the patient's chest that features three unique ECG vectors. Fromthese electrical signals the ECG circuit determines up to three ECGwaveforms, which are digitized using an analog-to-digital convertermounted proximal to the ECG circuit, and sent through a five-wire cable282 to the wrist-worn transceiver 272 according to the CAN protocol.There, the ECG is processed with the PPG to determine the patient'sblood pressure. Heart rate and respiratory rate are determined directlyfrom the ECG waveform using known algorithms, such as those described inthe following reference, the contents of which are incorporated hereinby reference: ‘ECG Beat Detection Using Filter Banks’, Afonso et al.,IEEE Trans. Biomed Eng., 46:192-202 (1999). The cable bulkhead 274 alsoincludes an accelerometer that measures motion associated with thepatient's chest as described above.

There are several advantages of digitizing ECG and ACC waveforms priorto transmitting them through the cable 282. First, a single transmissionline in the cable 282 can transmit multiple digital waveforms, eachgenerated by different sensors. This includes multiple ECG waveforms(corresponding, e.g., to vectors associated with three, five, andtwelve-lead ECG systems) from the ECG circuit mounted in the bulkhead274, along with waveforms associated with the x, y, and z axes ofaccelerometers mounted in the bulkheads 275, 296. Limiting thetransmission line to a single cable reduces the number of wires attachedto the patient, thereby decreasing the weight and cable-related clutterof the body-worn monitor. Second, cable motion induced by an ambulatorypatient can change the electrical properties (e.g. electrical impedance)of its internal wires. This, in turn, can add noise to an analog signaland ultimately the vital sign calculated from it. A digital signal, incontrast, is relatively immune to such motion-induced artifacts.

More sophisticated ECG circuits can plug into the wrist-worn transceiverto replace the three-lead system shown in FIGS. 20A and 20B. These ECGcircuits can include, e.g., five and twelve leads.

FIG. 21 shows a close-up view of the wrist-worn transceiver 272. Asdescribed above, it attaches to the patient's wrist using a flexiblestrap 290 which threads through two D-ring openings in a plastic housing206. The transceiver 272 features a touchpanel display 200 that rendersa GUI 273, similar to that shown in FIGS. 11A,B, which is altereddepending on the viewer (typically the patient or a medicalprofessional). Specifically, the transceiver 272 includes a small-scaleinfrared barcode scanner 202 that, during use, can scan a barcode wornon a badge of a medical professional. The barcode indicates to thetransceiver's software that, for example, a nurse or doctor is viewingthe user interface. In response, the GUI 273 displays vital sign dataand other medical diagnostic information appropriate for medicalprofessionals. Using this GUI 273, the nurse or doctor, for example, canview the vital sign information, set alarm parameters, and enterinformation about the patient (e.g. their demographic information,medication, or medical condition). The nurse can press a button on theGUI 273 indicating that these operations are complete. At this point,the display 200 renders an interface that is more appropriate to thepatient, e.g. something similar to FIG. 11A that displays parameterssimilar to those from a conventional wristwatch, such as time of day andbattery power.

As described above, the transceiver 272 features three CAN connectors204 a-c on the side of its upper portion, each which supports the CANprotocol and wiring schematics, and relays digitized data to theinternal CPU. Digital signals that pass through the CAN connectorsinclude a header that indicates the specific signal (e.g. ECG, ACC, orpressure waveform from the cuff-based module) and the sensor from whichthe signal originated. This allows the CPU to easily interpret signalsthat arrive through the CAN connectors 204 a-c, and means that theseconnectors are not associated with a specific cable. Any cableconnecting to the transceiver can be plugged into any connector 204 a-c.As shown in FIG. 20A, the first connector 204 a receives the five-wirecable 282 that transports a digitized ECG waveform determined from theECG circuit and electrodes, and digitized ACC waveforms measured byaccelerometers in the cable bulkhead 274 and the bulkhead portion 296associated with the ECG cable 282.

The second CAN connector 204 b shown in FIG. 21 receives the cable 286that connects to the pneumatic cuff-based system 285 used for thepressure-dependent indexing measurement. This connector receives atime-dependent pressure waveform delivered by the pneumatic system 285to the patient's arm, along with values for SYS, DIA, and MAP valuesdetermined during the indexing measurement. The cable 286 unplugs fromthe connector 204 b once the indexing measurement is complete, and isplugged back in after approximately four hours for another indexingmeasurement.

The final CAN connector 204 c can be used for an ancillary device, e.g.a glucometer, infusion pump, body-worn insulin pump, ventilator, orend-tidal CO₂ delivery system. As described above, digital informationgenerated by these systems will include a header that indicates theirorigin so that the CPU can process them accordingly.

The transceiver includes a speaker 201 that allows a medicalprofessional to communicate with the patient using a voice over Internetprotocol (VOIP). For example, using the speaker 101 the medicalprofessional could query the patient from a central nursing station ormobile phone connected to a wireless, Internet-based network within thehospital. Or the medical professional could wear a separate transceiversimilar to the shown in FIG. 21, and use this as a communication device.In this application, the transceiver 272 worn by the patient functionsmuch like a conventional cellular telephone or ‘walkie talkie’: it canbe used for voice communications with the medical professional and canadditionally relay information describing the patient's vital signs andmotion.

In addition to those methods described above, a number of additionalmethods can be used to calculate blood pressure from the optical andelectrical waveforms. These are described in the following co-pendingpatent applications, the contents of which are incorporated herein byreference: 1) CUFFLESS BLOOD-PRESSURE MONITOR AND ACCOMPANYING WIRELESS,INTERNET-BASED SYSTEM (U.S. Ser. No. 10/709,015; filed Apr. 7, 2004); 2)CUFFLESS SYSTEM FOR MEASURING BLOOD PRESSURE (U.S. Ser. No. 10/709,014;filed Apr. 7, 2004); 3) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYINGWEB SERVICES INTERFACE (U.S. Ser. No. 10/810,237; filed Mar. 26, 2004);4) VITAL SIGN MONITOR FOR ATHLETIC APPLICATIONS (U.S.S.N.; filed Sep.13, 2004); 5) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYING WIRELESSMOBILE DEVICE (U.S. Ser. No. 10/967,511; filed Oct. 18, 2004); 6) BLOODPRESSURE MONITORING DEVICE FEATURING A CALIBRATION-BASED ANALYSIS (U.S.Ser. No. 10/967,610; filed Oct. 18, 2004); 7) PERSONAL COMPUTER-BASEDVITAL SIGN MONITOR (U.S. Ser. No. 10/906,342; filed Feb. 15, 2005); 8)PATCH SENSOR FOR MEASURING BLOOD PRESSURE WITHOUT A CUFF (U.S. Ser. No.10/906,315; filed Feb. 14, 2005); 9) PATCH SENSOR FOR MEASURING VITALSIGNS (U.S. Ser. No. 11/160,957; filed Jul. 18, 2005); 10) WIRELESS,INTERNET-BASED SYSTEM FOR MEASURING VITAL SIGNS FROM A PLURALITY OFPATIENTS IN A HOSPITAL OR MEDICAL CLINIC (U.S. Ser. No. 11/162,719;filed Sep. 9, 2005); 11) HAND-HELD MONITOR FOR MEASURING VITAL SIGNS(U.S. Ser. No. 11/162,742; filed Sep. 21, 2005); 12) CHEST STRAP FORMEASURING VITAL SIGNS (U.S. Ser. No. 11/306,243; filed Dec. 20, 2005);13) SYSTEM FOR MEASURING VITAL SIGNS USING AN OPTICAL MODULE FEATURING AGREEN LIGHT SOURCE (U.S. Ser. No. 11/307,375; filed Feb. 3, 2006); 14)BILATERAL DEVICE, SYSTEM AND METHOD FOR MONITORING VITAL SIGNS (U.S.Ser. No. 11/420,281; filed May 25, 2006); 15) SYSTEM FOR MEASURING VITALSIGNS USING BILATERAL PULSE TRANSIT TIME (U.S. Ser. No. 11/420,652;filed May 26, 2006); 16) BLOOD PRESSURE MONITOR (U.S. Ser. No.11/530,076; filed Sep. 8, 2006); 17) TWO-PART PATCH SENSOR FORMONITORING VITAL SIGNS (U.S. Ser. No. 11/558,538; filed Nov. 10, 2006);and, 18) MONITOR FOR MEASURING VITAL SIGNS AND RENDERING VIDEO IMAGES(U.S. Ser. No. 11/682,177; filed Mar. 5, 2007).

Other embodiments are also within the scope of the invention. Forexample, other techniques, such as conventional oscillometry measuredduring deflation, can be used to determine SYS for the above-describedalgorithms.

Still other embodiments are within the scope of the following claims.

What is claimed is:
 1. A method for generating an alarm while monitoringvital signs and posture of a patient, the method comprising thefollowing steps: (a) detecting a first time-dependent physiologicalwaveform indicative of one or more contractile properties of thepatient's heart with a first sensor comprising a first detectorconfigured to be worn on the patient's body; (b) detecting a secondtime-dependent physiological waveform indicative of one or morecontractile properties of the patient's heart with a second sensorcomprising a second detector configured to be worn on the patient'sbody; (c) detecting a set of time-dependent motion waveforms with atleast one motion-detecting sensor positioned on the patient's torso,wherein the set of time-dependent motion waveforms are indicative ofmotion of the patient's torso; (d) receiving and processing the firstand second time-dependent physiological waveforms and the set oftime-dependent motion waveforms, and generating an alarm therefromindicative of a need for medical attention, using one or more processingcomponents comprising a microprocessor, the one or more processingcomponents configured to (i) process the first and second time-dependentphysiological waveforms to determine at least one vital sign from thepatient; (ii) analyze at least a portion of the set of time-dependentmotion waveforms, or a mathematical derivative thereof, to determine avector corresponding to motion of the patient's torso; (iii) compare thevector to a coordinate space representative of how the motion-detectingsensor is oriented on the patient to determine a posture parameter; (iv)compare the posture parameter to a threshold value to estimate thepatient's posture; (v) compare the at least one vital sign from thepatient to a predetermined alarm criteria, wherein an alarm is indicatedby a variance of the vital sign relative to the predetermined alarmcriteria to determine an alarm parameter; and (vi) generate the alarm bycollectively processing the patient's posture and the alarm parameter,wherein the alarm indicated by a variance of the vital sign relative tothe predetermined alarm criteria is regulated according to the patient'sposture.
 2. The method of claim 1, wherein the set of time-dependentmotion waveforms comprises three time-dependent motion waveforms, eachcorresponding to a unique axis, and wherein the vector corresponding tomotion of the patient's torso is determined from the threetime-dependent motion waveforms.
 3. The method of claim 2, wherein thecoordinate space representative of how the motion-detecting sensor isoriented on the patient is defined by three positional vectors, eachindicating an orientation of the motion-detecting sensor on the patient.4. The method of claim 3, wherein a first positional vector of the threepositional vectors corresponds to a vertical axis in the coordinatespace, a second positional vector of the three positional vectorscorresponds to a horizontal axis in the coordinate space, and a thirdpositional vector of the three positional vectors corresponds to anormal axis extending normal to the patient's chest.
 5. The method ofclaim 3, wherein the posture parameter is an angle.
 6. The method ofclaim 5, wherein step (f) further comprises comparing the vector to thecoordinate space to determine the angle between the vector and at leastone of the three positional vectors.
 7. The method of claim 6, whereinstep (d)(iii) further comprises comparing the vector to the coordinatespace to determine the angle between the vector and the first positionalvector.
 8. The method of claim 7, wherein step (d)(iv) further comprisesestimating the patient's posture to be upright if the angle between thevector and the first positional vector is less than 45 degrees.
 9. Themethod of claim 7, wherein step (d)(iv) further comprises estimating thepatient's posture to be lying down if the angle between the vector andthe first positional vector is greater than 45 degrees.
 10. The methodof claim 9, wherein step (d)(iii) further comprises comparing the vectorto the coordinate space to determine the angle between the vector andthe third positional vector.
 11. The method of claim 10, wherein step(d)(iv) further comprises estimating the patient's posture to be supineif the angle between the vector and the third positional vector is lessthan 35 degrees.
 12. The method of claim 10, wherein step (d)(iv)further comprises estimating the patient's posture to be prone if theangle between the vector and the third positional vector is greater than135 degrees.
 13. The method of claim 9, wherein step (d)(iii) furthercomprises comparing the vector to the coordinate space to determine theangle between the vector and the second positional vector.
 14. Themethod of claim 13, wherein step (d)(iv) further comprises estimatingthe patient's posture to be lying on a first side if the angle betweenthe vector and the third positional vector is less than 90 degrees. 15.The method of claim 13, wherein step (d)(iv) further comprisesestimating the patient's posture to be lying on a side opposite thefirst side if the angle between the vector and the second positionalvector is greater than 90 degrees.
 16. The method of claim 1, wherein atleast one motion-detecting sensor positioned on the patient's torso ispositioned on the patient's chest.
 17. The method of claim 1, where theat least one motion-detecting sensor positioned on the patient's torsois an accelerometer.
 18. The method of claim 1, wherein step (d) furthercomprises processing the first and second time-dependent physiologicalwaveforms to calculate a time difference between a feature in the firsttime-dependent physiological waveform and a feature in the secondtime-dependent physiological waveform.
 19. The method of claim 18,wherein step (d) further comprises calculating a blood pressure from thetime difference.
 20. The method of claim 18, wherein the firsttime-dependent physiological waveform is an electrical waveform, andstep (a) further comprises detecting the electrical waveform with atleast two electrodes attached to the patient and operably connected toan electrical circuit.
 21. The method of claim 20, wherein the secondtime-dependent physiological waveform is an optical waveform, and step(b) further comprises detecting the optical waveform with an opticalsensor comprising a light source and a photodiode.
 22. The method ofclaim 18, wherein each of the first and second time-dependentphysiological waveforms are selected from a group consisting of anoptical waveform, an electrical waveform, an acoustic waveform, and apressure waveform.
 23. The method of claim 22, wherein steps (a) and (b)each further comprise detecting a time-dependent physiological waveformwith a sensor selected from a group consisting of: (i) an optical sensorcomprising a light source and a photodiode; (ii) an electrical sensorcomprising at least two electrodes operably connected to an electricalcircuit; (iii) an acoustic sensor comprising a microphone; and (iv) apressure sensor comprising a transducer.